diff --git ant/src/org/apache/hadoop/hive/ant/GenVectorCode.java ant/src/org/apache/hadoop/hive/ant/GenVectorCode.java index 6c57da2..2e4d365 100644 --- ant/src/org/apache/hadoop/hive/ant/GenVectorCode.java +++ ant/src/org/apache/hadoop/hive/ant/GenVectorCode.java @@ -24,6 +24,10 @@ import java.io.FileReader; import java.io.FileWriter; import java.io.IOException; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; import org.apache.tools.ant.BuildException; import org.apache.tools.ant.Task; @@ -848,6 +852,19 @@ "Math.sqrt(myagg.variance / (myagg.count-1.0))", "stddev_samp", "_FUNC_(x) - Returns the sample standard deviation of a set of numbers (vectorized, decimal)"}, + // VectorGroupBy template, [, ] + + // Count any kind of column type + {"ColAggrCountOnly", "Arrays"}, + + // Generate classes for the permutation of aggregation functions. + {"ColAggr", "Arrays", "Long", "Min,Max,Count,Sum"}, + {"ColAggr", "Arrays", "Double", "DoubleMin,DoubleMax,Count,DoubleSum"}, + + // Generate map from aggregation function permutations to column aggregation class. + {"ColAggrMap", "Arrays", "Long", "Min,Max,Count,Sum"}, + {"ColAggrMap", "Arrays", "Double", "DoubleMin,DoubleMax,Count,DoubleSum"}, + }; @@ -860,6 +877,9 @@ private String udafOutputDirectory; private String udafClassesDirectory; private String udafTemplateDirectory; + private String groupByAggregationOutputDirectory; + private String groupByAggregationClassesDirectory; + private String groupByAggregationTemplateDirectory; private GenVectorTestCode testCodeGen; static String joinPath(String...parts) { @@ -896,6 +916,16 @@ public void init(String templateBaseDir, String buildDir) { udafTemplateDirectory = joinPath(generationDirectory.getAbsolutePath(), "UDAFTemplates"); + String groupByAggregation = joinPath("org", "apache", "hadoop", + "hive", "ql", "exec", "vector", "groupby", "aggregation", "gen"); + File groupByAggregationOutput = new File(joinPath(buildPath, groupByAggregation)); + File groupByAggregationClasses = new File(joinPath(compiledPath, groupByAggregation)); + groupByAggregationOutputDirectory = groupByAggregationOutput.getAbsolutePath(); + groupByAggregationClassesDirectory = groupByAggregationClasses.getAbsolutePath(); + + groupByAggregationTemplateDirectory = + joinPath(generationDirectory.getAbsolutePath(), "GroupByAggregationTemplates"); + File testCodeOutput = new File( joinPath(buildDir, "generated-test-sources", "java", "org", @@ -1075,6 +1105,12 @@ private void generate() throws Exception { generateDateTimeScalarArithmeticIntervalColumnWithConvert(tdesc); } else if (tdesc[0].equals("IntervalColumnArithmeticDateTimeScalarWithConvert")) { generateDateTimeColumnArithmeticIntervalScalarWithConvert(tdesc); + } else if (tdesc[0].equals("ColAggrCountOnly")) { + generateColAggrCountOnly(tdesc); + } else if (tdesc[0].equals("ColAggr")) { + generateColAggr(tdesc); + } else if (tdesc[0].equals("ColAggrMap")) { + generateColAggrMap(tdesc); } else { continue; } @@ -2702,6 +2738,716 @@ private static boolean isDateTimeIntervalType(String type) { || type.equals("interval_day_time")); } + /** + * + * These comments cover the class generation for Vector GROUP BY. + * + * Class are generated to calculate specific sets of aggregations for a vector column for + * a hash table entry. + * + * For example, VectorGroupByColAggrLongNonSelectedNoNullsMinCountSum in the generated + * package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen will do aggregation + * for the MIN, COUNT(column), and SUM aggregation functions on a LongColumnVector. + * + * The base abstract class is VectorGroupByColAggr. The apply method will perform the + * aggregations for consecutive rows that all have the same key. The hash table entry + * (already looked up) passes the first aggregation offset to calculate with using the + * colBaseAggrLongIndex parameter. + * + * The pattern in the class name is: + * VectorGroupByColAggr + + + + * e.g. VectorGroupByColAggr + Long NonSelectedNoNulls MinCountSum + * + * Current vector column types are Long and Double. + * + * NOTE: The columns are non-key columns. Since key columns are equal for the hash table entry, + * their aggregation is trivial and handled in general classes in the VectorGroupByCommonOutput + * class at hash table flush time (not hash key lookup time). + * + * Since a batch's (input) column has fixed flags for isRepeated, selectedInUse, and noNulls -- + * we determine the batch variation at the beginning of processing a batch. For better + * performance we do not want to have to check those fixed flags inside our column aggregation + * function each time. + * + * The simplest batch variation is RepeatingNoNulls. Vector entry 0 is defined for isRepeating + * to be the value for the whole column (regardless of the selected array). + * + * SelectedNulls is perhaps the most complicated because it has to go through the selected array + * to get to each row and has to then check for null. + * + * Classes are generated by the ColAggr.txt templates. + * + * ----------------------------------------------------------------------------------------------- + * + * Since a COUNT(column) by itself can operate on any ColumnVector, those generated classes + * have the form without the : + * VectorGroupByColAggr + + Count + * + * It is generated by the ColAggrCount.txt templates. + * The code for just a COUNT(column) is simpler than that mix in MIN, MAX, SUM, etc. + * + * ----------------------------------------------------------------------------------------------- + * + * The ColAggrMap template is used to generate a mapping from a number that represents + * the set of aggregation functions (MIN, MAX, SUM) to an array of the batch variation classes + * that will do the aggregation(s). + * + * ----------------------------------------------------------------------------------------------- + * + * The aggregations functions are ordered so map lookup will work and so the aggregation + * calculations can be done in the same order in all classes so the aggregation offsets are + * deterministic. That is, MIN will always be done before a MAX (if there is one) and + * MAX before COUNT, etc. + * + * So that is why we generate ordered permutations below. + * + */ + + // NOTE: Copy of enums from VectorGroupByColAggr since we can't reference the definition here. + + public static enum ColumnVectorType { + NONE, + LONG, + DOUBLE, + BYTES, + DECIMAL + } + + // ORDER SENSITIVE --> Column aggregations does these basic aggregate operations in this exact + // order: + public enum BasicAggregation { + LONG_MIN ("Min", "Min", "longMin", 0, 1 << 0, ColumnVectorType.LONG), + DOUBLE_MIN ("DoubleMin", "Min", "doubleMin", 1, 1 << 1, ColumnVectorType.DOUBLE), + LONG_MAX ("Max", "Max", "longMax", 2, 1 << 2, ColumnVectorType.LONG), + DOUBLE_MAX ("DoubleMax", "Max", "doubleMax", 3, 1 << 3, ColumnVectorType.DOUBLE), + COUNT ("Count", "Count", "count", 4, 1 << 4, ColumnVectorType.LONG), + LONG_SUM ("Sum", "Sum", "longSum", 5, 1 << 5, ColumnVectorType.LONG), + DOUBLE_SUM ("DoubleSum", "Sum", "doubleSum", 6, 1 << 6, ColumnVectorType.DOUBLE); + + public final String string; + public final String simpleName; + public final String variableName; + public final int value; + public final int mask; + public final ColumnVectorType columnVectorType; + + public static final BasicAggregation[] basicAggregationValues = BasicAggregation.values(); + + BasicAggregation(String string, String simpleName, String variableName, + int value, int mask, ColumnVectorType columnVectorType) { + this.string = string; + this.simpleName = simpleName; + this.variableName = variableName; + this.value = value; + this.mask = mask; + this.columnVectorType = columnVectorType; + } + } + + + public enum BatchVariation { + REPEATING_NONULLS (0, "RepeatingNoNulls"), + REPEATING_NULLS (1, "RepeatingNulls"), + SELECTED_NONULLS (2, "SelectedNoNulls"), + SELECTED_NULLS (3, "SelectedNulls"), + NONSELECTED_NONULLS (4, "NonSelectedNoNulls"), + NONSELECTED_NULLS (5, "NonSelectedNulls"); + + public final int index; + public final String string; + + public static final BatchVariation[] batchVariationValues = BatchVariation.values(); + + BatchVariation(int index, String string) { + this.index = index; + this.string = string; + } + } + + public static final Map stringToAggregationMap = + new HashMap(); + static { + for (BasicAggregation aggregation : BasicAggregation.values()) { + stringToAggregationMap.put(aggregation.string, aggregation); + } + } + + private void orderedPermutationsRecursion(List input, + List> accumulate) { + List firstList = input.subList(0, 1); + List firstRemoved = input.subList(1, input.size()); + if (firstRemoved.size() == 0) { + accumulate.add(firstList); + accumulate.add(firstRemoved); + } else { + List> subAccumulate = new ArrayList>(); + orderedPermutationsRecursion(firstRemoved, subAccumulate); + accumulate.addAll(subAccumulate); + for (List variation : subAccumulate) { + List newList = new ArrayList(firstList); + newList.addAll(variation); + accumulate.add(newList); + } + } + } + private List> orderedPermutations(List input) { + List> accumulate = new ArrayList>(); + orderedPermutationsRecursion(input, accumulate); + return accumulate; + } + + private void generateColAggrCountOnly(String[] tdesc) throws Exception { + String templateName = tdesc[0]; + String memoryRepresentation = tdesc[1]; + List singlePermutation = new ArrayList(1); + singlePermutation.add(BasicAggregation.COUNT); + generateColAggrPermutation(templateName, memoryRepresentation, "None", singlePermutation); + } + + private void generateColAggr(String[] tdesc) throws Exception { + String templateName = tdesc[0]; + String memoryRepresentation = tdesc[1]; + String columnVectorType = tdesc[2]; + String[] aggregationStringsSplit = tdesc[3].split(","); + List aggregationList = new ArrayList(); + for (String aggregationString : aggregationStringsSplit) { + aggregationList.add(stringToAggregationMap.get(aggregationString)); + } + List> permutations = orderedPermutations(aggregationList); + for (List permutation : permutations) { + + // When the permutation is only COUNT(column), it is handled separately by the + // count any column type classes. + if (permutation.size() == 0 || + (permutation.size() == 1 && permutation.get(0) == BasicAggregation.COUNT)) { + continue; + } + generateColAggrPermutation(templateName, memoryRepresentation, columnVectorType, + permutation); + } + } + + private String makeCombinedAggregateSimpleNames(List permutation) { + StringBuilder sb = new StringBuilder(); + for (BasicAggregation aggregate : permutation) { + sb.append(aggregate.simpleName); + } + return sb.toString(); + } + + private static String LONG_TO_DOUBLE_FUNC = "Double.longBitsToDouble"; + private static String DOUBLE_TO_LONG_FUNC = "Double.doubleToLongBits"; + + private void generateColAggrPermutation(String templateName, String memoryRepresentation, + String columnVectorTypeName, List permutation) throws Exception { + + boolean isOnlyCount = + (permutation.size() == 1 && permutation.get(0) == BasicAggregation.COUNT); + + String primitiveTypeName = columnVectorTypeName.toLowerCase(); + + String valueName = primitiveTypeName + "Value"; + + String vectorName = primitiveTypeName + "Vector"; + + String columnVectorTypeClass = columnVectorTypeName + "ColumnVector"; + + StringBuilder sb = new StringBuilder(); + sb.append("{"); + boolean isFirst = true; + for (BasicAggregation aggregate : permutation) { + if (!isFirst) { + sb.append(","); + } else { + isFirst = false; + } + sb.append(aggregate.string); + } + sb.append("}"); + String aggregateCommentSet = sb.toString(); + + String aggregateCount = ((Integer) permutation.size()).toString(); + + String combinedAggregateNames = makeCombinedAggregateSimpleNames(permutation); + + String firstAggregateVarAssign; + String additionalAggregateVarsCopy; + String loopAggregation; + + sb = new StringBuilder(); + String firstVariableName = ""; + for (BasicAggregation aggregation : permutation) { + if (aggregation == BasicAggregation.COUNT) { + // Uses loop counter. + continue; + } + if (firstVariableName.length() == 0) { + firstVariableName = aggregation.variableName; + } else { + sb.append(primitiveTypeName + " " + aggregation.variableName + " = " + firstVariableName + ";\n"); + } + } + firstAggregateVarAssign = primitiveTypeName + " " + firstVariableName; + additionalAggregateVarsCopy = sb.toString(); + + sb = new StringBuilder(); + for (BasicAggregation aggregation : permutation) { + String variableName = aggregation.variableName; + switch (aggregation) { + case LONG_MIN: + case DOUBLE_MIN: + sb.append(variableName); + sb.append(" = Math.min("); + sb.append(valueName); + sb.append(", "); + sb.append(variableName); + sb.append(");\n"); + break; + case LONG_MAX: + case DOUBLE_MAX: + sb.append(variableName); + sb.append(" = Math.max("); + sb.append(valueName); + sb.append(", "); + sb.append(variableName); + sb.append(");\n"); + break; + case COUNT: + // Uses loop counter. + break; + case LONG_SUM: + case DOUBLE_SUM: + sb.append(variableName + " += " + valueName + ";\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + loopAggregation = sb.toString(); + + // The following vary for memory representation. + String memoryRepresentationLongAggrEntryVarDeclare; + String memoryRepresentationLongAggrEntryVar; + + String aggregateInitialSingle; + String aggregateAccumSingle; + + String aggregateInitialRepeat; + String aggregateAccumRepeat; + + String aggregateInitialMultiDuplicateCount; + String aggregateAccumMultiDuplicateCount; + + String aggregateInitialMultiNonNullCount; + String aggregateAccumMultiNonNullCount; + + if (memoryRepresentation.equals("Arrays")) { + memoryRepresentationLongAggrEntryVarDeclare = + "// Hash table entry storage array.\n" + + "private long[] longs;"; + memoryRepresentationLongAggrEntryVar = "longs = aggrEntryAccess.getLongs();"; + + // 1. Initialize single + sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + switch (aggregation) { + case LONG_MIN: + case LONG_MAX: + case LONG_SUM: + sb.append(longsRef); + sb.append(" = " + valueName + ";\n"); + break; + case DOUBLE_MIN: + case DOUBLE_MAX: + case DOUBLE_SUM: + sb.append(longsRef); + sb.append(" = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append(" = 1;\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + aggregateInitialSingle = sb.toString(); + + // 2. Accumulate single + sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + switch (aggregation) { + case LONG_MIN: + sb.append(longsRef); + sb.append(" = Math.min("); + sb.append(longsRef); + sb.append(", " + valueName + ");\n"); + break; + case DOUBLE_MIN: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") < " + valueName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + sb.append("}\n"); + break; + case LONG_MAX: + sb.append(longsRef); + sb.append(" = Math.max("); + sb.append(longsRef); + sb.append(", " + valueName + ");\n"); + break; + case DOUBLE_MAX: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + " (" + longsRef + ") > " + valueName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + sb.append("}\n"); + break; + case LONG_SUM: + sb.append(longsRef); + sb.append(" += " + valueName + ";\n"); + break; + case DOUBLE_SUM: + sb.append(longsRef + " = " + DOUBLE_TO_LONG_FUNC + "("); + sb.append(LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") + " + valueName); + sb.append(");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append("++;\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + aggregateAccumSingle = sb.toString(); + + // 3. Initialize repeat + sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + switch (aggregation) { + case LONG_MIN: + case LONG_MAX: + sb.append(longsRef); + sb.append(" = " + valueName + ";\n"); + break; + case DOUBLE_MIN: + case DOUBLE_MAX: + sb.append(longsRef); + sb.append(" = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + break; + case LONG_SUM: + sb.append(longsRef); + sb.append(" = duplicateCount * " + valueName + ";\n"); + break; + case DOUBLE_SUM: + sb.append(longsRef); + sb.append(" = " + DOUBLE_TO_LONG_FUNC + "("); + sb.append("(double) duplicateCount * " + valueName); + sb.append(");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append(" = duplicateCount;\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + aggregateInitialRepeat = sb.toString(); + + // 4. Accumulate repeat + sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + switch (aggregation) { + case LONG_MIN: + sb.append(longsRef); + sb.append(" = Math.min("); + sb.append(longsRef); + sb.append(", " + valueName + ");\n"); + break; + case DOUBLE_MIN: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") < " + valueName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + sb.append("}\n"); + break; + case LONG_MAX: + sb.append(longsRef); + sb.append(" = Math.max("); + sb.append(longsRef); + sb.append(", " + valueName + ");\n"); + break; + case DOUBLE_MAX: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") > " + valueName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + valueName + ");\n"); + sb.append("}\n"); + break; + case LONG_SUM: + sb.append(longsRef); + sb.append(" += duplicateCount * longValue;\n"); + break; + case DOUBLE_SUM: + sb.append(longsRef); + sb.append(" = " + DOUBLE_TO_LONG_FUNC + "("); + sb.append(LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") + "); + sb.append("(double) duplicateCount * " + valueName); + sb.append(");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append(" += duplicateCount;\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + aggregateAccumRepeat = sb.toString(); + + // 5 & 6. + aggregateInitialMultiDuplicateCount = aggregateInitialMulti(permutation, "duplicateCount"); + aggregateAccumMultiDuplicateCount = aggregateAccumMulti(permutation, "duplicateCount"); + + // 7 & 8. + aggregateInitialMultiNonNullCount = aggregateInitialMulti(permutation, "nonNullCount"); + aggregateAccumMultiNonNullCount = aggregateAccumMulti(permutation, "nonNullCount"); + + } else { + throw new RuntimeException("Memory representation " + memoryRepresentation + " not implemented"); + } + + for (BatchVariation batchVariation : BatchVariation.batchVariationValues) { + String variantTemplateName = "ColAggr" + batchVariation.string + (isOnlyCount ? "Count" : ""); + + //Read the template into a string; + File templateFile = new File(joinPath(this.groupByAggregationTemplateDirectory, variantTemplateName + ".txt")); + String templateString = readFile(templateFile); + + String className = "VectorGroupByColAggr" + (isOnlyCount ? "" : columnVectorTypeName) + batchVariation.string + combinedAggregateNames; + + templateString = templateString.replaceAll("", aggregateCommentSet); + templateString = templateString.replaceAll("", aggregateCount); + + templateString = templateString.replaceAll("", className); + + templateString = templateString.replaceAll("", memoryRepresentationLongAggrEntryVarDeclare); + templateString = templateString.replaceAll("", memoryRepresentationLongAggrEntryVar); + + templateString = templateString.replaceAll("", primitiveTypeName); + templateString = templateString.replaceAll("", valueName); + templateString = templateString.replaceAll("", vectorName); + templateString = templateString.replaceAll("", columnVectorTypeClass); + + templateString = templateString.replaceAll("", firstAggregateVarAssign); + templateString = templateString.replaceAll("", additionalAggregateVarsCopy); + templateString = templateString.replaceAll("", loopAggregation); + + templateString = templateString.replaceAll("", aggregateInitialSingle); + templateString = templateString.replaceAll("", aggregateAccumSingle); + + templateString = templateString.replaceAll("", aggregateInitialRepeat); + templateString = templateString.replaceAll("", aggregateAccumRepeat); + + templateString = templateString.replaceAll("", aggregateInitialMultiDuplicateCount); + templateString = templateString.replaceAll("", aggregateAccumMultiDuplicateCount); + + templateString = templateString.replaceAll("", aggregateInitialMultiNonNullCount); + templateString = templateString.replaceAll("", aggregateAccumMultiNonNullCount); + + writeFile(templateFile.lastModified(), groupByAggregationOutputDirectory, groupByAggregationClassesDirectory, + className, templateString); + } + } + + private String aggregateInitialMulti(List permutation, + String counterVar) throws Exception { + + StringBuilder sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + String variableName = aggregation.variableName; + switch (aggregation) { + case LONG_MIN: + case LONG_MAX: + case LONG_SUM: + sb.append(longsRef); + sb.append(" = " + variableName + ";\n"); + break; + case DOUBLE_MIN: + case DOUBLE_MAX: + case DOUBLE_SUM: + sb.append(longsRef); + sb.append(" = " + DOUBLE_TO_LONG_FUNC + "(" + variableName + ");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append(" = "); + sb.append(counterVar); + sb.append(";\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + return sb.toString(); + } + + private String aggregateAccumMulti(List permutation, + String counterVar) throws Exception { + + StringBuilder sb = new StringBuilder(); + for (int i = 0; i < permutation.size(); i++) { + BasicAggregation aggregation = permutation.get(i); + String colBaseAggrLongIndex = (i == 0 ? "colBaseAggrLongIndex" : "colBaseAggrLongIndex + " + ((Integer) i).toString()); + String longsRef = "longs[" + colBaseAggrLongIndex + "]"; + String variableName = aggregation.variableName; + switch (aggregation) { + case LONG_MIN: + sb.append(longsRef); + sb.append(" = Math.min("); + sb.append(longsRef); + sb.append(", " + variableName + ");\n"); + break; + case DOUBLE_MIN: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") < " + variableName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + variableName + ");\n"); + sb.append("}\n"); + break; + case LONG_MAX: + sb.append(longsRef); + sb.append(" = Math.max("); + sb.append(longsRef); + sb.append(", longMax);\n"); + break; + case DOUBLE_MAX: + sb.append("if (" + LONG_TO_DOUBLE_FUNC + "(" + longsRef + ") > " + variableName + ") {\n"); + sb.append(" " + longsRef + " = " + DOUBLE_TO_LONG_FUNC + "(" + variableName + ");\n"); + sb.append("}\n"); + break; + case LONG_SUM: + sb.append(longsRef); + sb.append(" += longSum;\n"); + break; + case DOUBLE_SUM: + sb.append(longsRef + " = " + DOUBLE_TO_LONG_FUNC + "("); + sb.append(LONG_TO_DOUBLE_FUNC + " (" + longsRef + ") + " + variableName); + sb.append(");\n"); + break; + case COUNT: + sb.append(longsRef); + sb.append(" += "); + sb.append(counterVar); + sb.append(";\n"); + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregation.name()); + } + } + return sb.toString(); + } + + private void generateColAggrMap(String[] tdesc) + throws Exception { + + + String templateName = tdesc[0]; + String memoryRepresentation = tdesc[1]; + String columnVectorType = tdesc[2]; + String[] aggregationStringsSplit = tdesc[3].split(","); + List aggregationList = new ArrayList(); + for (String aggregationString : aggregationStringsSplit) { + aggregationList.add(stringToAggregationMap.get(aggregationString)); + } + + String className = "VectorGroupByColAggr" + columnVectorType + "Map"; + + String mapVariableName = columnVectorType.toLowerCase() + "ColAggrMap"; + + StringBuilder sb = new StringBuilder(); + List> permutations = orderedPermutations(aggregationList); + for (List permutation : permutations) { + if (permutation.size() == 0) { + continue; + } + + boolean isCountOnly = (permutation.size() == 1 && permutation.get(0) == BasicAggregation.COUNT); + + String aggregateCommentSet = permutation.toString(); + String combinedAggregateNames = makeCombinedAggregateSimpleNames(permutation); + + int mask = 0; + for (BasicAggregation aggregation : permutation) { + mask |= aggregation.mask; + } + + // Example: + // + // [MIN,MAX,SUM] + // + // classesArrayList = new ArrayList>(batchVariationValues.length); + // classesArrayList.add( + // VectorGroupByColAggrRepeatingNoNullsLongMinMaxSum.class); + // classesArrayList.add( + // VectorGroupByColAggrRepeatingNullsLongMinMaxSum.class); + // classesArrayList.add( + // VectorGroupByColAggrSelectedNoNullsLongMinMaxSum.class); + // classesArrayList.add( + // VectorGroupByColAggrSelectedNullsLongMinMaxSum.class); + // classesArrayList.add( + // VectorGroupByColAggrNonSelectedNoNullsLongMinMaxSum.class); + // classesArrayList.add( + // VectorGroupByColAggrNonSelectedNullsLongMinMaxSum.class); + // longColAggrMap.put(34, classesArrayList); + // + + sb.append("// "); + sb.append(aggregateCommentSet); + sb.append("\n"); + sb.append("//\n"); + + sb.append("classesArrayList =\n"); + sb.append(" new ArrayList>(BatchVariation.batchVariationValues.length);\n"); + for (BatchVariation batchVariation : BatchVariation.values()) { + sb.append("classesArrayList.add(\n"); + sb.append(" VectorGroupByColAggr"); + if (!isCountOnly) { + sb.append(columnVectorType); + } + sb.append(batchVariation.string); + sb.append(combinedAggregateNames); + sb.append(".class"); + sb.append(");\n"); + } + sb.append(mapVariableName); + sb.append(".put("); + sb.append(((Integer) mask).toString()); + sb.append(", classesArrayList);\n"); + + sb.append("\n"); + } + String assignMapBody = sb.toString(); + + //Read the template into a string; + File templateFile = new File(joinPath(this.groupByAggregationTemplateDirectory, templateName + ".txt")); + String templateString = readFile(templateFile); + + templateString = templateString.replaceAll("", className); + templateString = templateString.replaceAll("", mapVariableName); + templateString = templateString.replaceAll("", assignMapBody); + + writeFile(templateFile.lastModified(), groupByAggregationOutputDirectory, groupByAggregationClassesDirectory, + className, templateString); + } + static void writeFile(long templateTime, String outputDir, String classesDir, String className, String str) throws IOException { File outputFile = new File(outputDir, className + ".java"); diff --git common/src/java/org/apache/hadoop/hive/conf/HiveConf.java common/src/java/org/apache/hadoop/hive/conf/HiveConf.java index 803d52b..f971fdd 100644 --- common/src/java/org/apache/hadoop/hive/conf/HiveConf.java +++ common/src/java/org/apache/hadoop/hive/conf/HiveConf.java @@ -2191,7 +2191,7 @@ public void setSparkConfigUpdated(boolean isSparkConfigUpdated) { HIVE_VECTORIZATION_ENABLED("hive.vectorized.execution.enabled", false, "This flag should be set to true to enable vectorized mode of query execution.\n" + "The default value is false."), - HIVE_VECTORIZATION_REDUCE_ENABLED("hive.vectorized.execution.reduce.enabled", true, + HIVE_VECTORIZATION_REDUCE_ENABLED("hive.vectorized.execution.reduce.enabled", false, "This flag should be set to true to enable vectorized mode of the reduce-side of query execution.\n" + "The default value is true."), HIVE_VECTORIZATION_REDUCE_GROUPBY_ENABLED("hive.vectorized.execution.reduce.groupby.enabled", true, @@ -2205,7 +2205,7 @@ public void setSparkConfigUpdated(boolean isSparkConfigUpdated) { "This flag should be set to true to restrict use of native vector map join hash tables to\n" + "the MultiKey in queries using MapJoin.\n" + "The default value is false."), - HIVE_VECTORIZATION_MAPJOIN_NATIVE_MINMAX_ENABLED("hive.vectorized.execution.mapjoin.minmax.enabled", false, + HIVE_VECTORIZATION_MAPJOIN_NATIVE_MINMAX_ENABLED("hive.vectorized.execution.mapjoin.minmax.enabled", true, "This flag should be set to true to enable vector map join hash tables to\n" + "use max / max filtering for integer join queries using MapJoin.\n" + "The default value is false."), @@ -2224,10 +2224,17 @@ public void setSparkConfigUpdated(boolean isSparkConfigUpdated) { "Exceeding this will trigger a flush irrelevant of memory pressure condition."), HIVE_VECTORIZATION_GROUPBY_FLUSH_PERCENT("hive.vectorized.groupby.flush.percent", (float) 0.1, "Percent of entries in the group by aggregation hash flushed when the memory threshold is exceeded."), + + HIVE_VECTORIZATION_GROUPBY_NATIVE_ENABLED("hive.vectorized.execution.groupby.native.enabled", true, + "This flag should be set to true to enable the native vectorization\n" + + "of queries using GroupBy.\n" + + "The default value is true."), + HIVE_VECTORIZATION_REDUCESINK_NEW_ENABLED("hive.vectorized.execution.reducesink.new.enabled", true, "This flag should be set to true to enable the new vectorization\n" + - "of queries using ReduceSink.\ni" + + "of queries using ReduceSink.\n" + "The default value is true."), + HIVE_TYPE_CHECK_ON_INSERT("hive.typecheck.on.insert", true, "This property has been extended to control " + "whether to check, convert, and normalize partition value to conform to its column type in " + "partition operations including but not limited to insert, such as alter, describe etc."), diff --git common/src/java/org/apache/hadoop/hive/conf/HiveConf.java.orig common/src/java/org/apache/hadoop/hive/conf/HiveConf.java.orig new file mode 100644 index 0000000..7f96071 --- /dev/null +++ common/src/java/org/apache/hadoop/hive/conf/HiveConf.java.orig @@ -0,0 +1,3420 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.conf; + +import com.google.common.base.Joiner; + +import org.apache.commons.lang.StringUtils; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.common.classification.InterfaceAudience.LimitedPrivate; +import org.apache.hadoop.hive.conf.Validator.PatternSet; +import org.apache.hadoop.hive.conf.Validator.RangeValidator; +import org.apache.hadoop.hive.conf.Validator.RatioValidator; +import org.apache.hadoop.hive.conf.Validator.StringSet; +import org.apache.hadoop.hive.conf.Validator.TimeValidator; +import org.apache.hadoop.hive.shims.Utils; +import org.apache.hadoop.mapred.JobConf; +import org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat; +import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; +import org.apache.hadoop.security.UserGroupInformation; +import org.apache.hadoop.util.Shell; +import org.apache.hive.common.HiveCompat; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import javax.security.auth.login.LoginException; + +import java.io.ByteArrayOutputStream; +import java.io.File; +import java.io.IOException; +import java.io.InputStream; +import java.io.PrintStream; +import java.net.URL; +import java.util.ArrayList; +import java.util.HashMap; +import java.util.HashSet; +import java.util.Iterator; +import java.util.List; +import java.util.Map; +import java.util.Map.Entry; +import java.util.Properties; +import java.util.Set; +import java.util.concurrent.TimeUnit; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +/** + * Hive Configuration. + */ +public class HiveConf extends Configuration { + protected String hiveJar; + protected Properties origProp; + protected String auxJars; + private static final Logger l4j = LoggerFactory.getLogger(HiveConf.class); + private static boolean loadMetastoreConfig = false; + private static boolean loadHiveServer2Config = false; + private static URL hiveDefaultURL = null; + private static URL hiveSiteURL = null; + private static URL hivemetastoreSiteUrl = null; + private static URL hiveServer2SiteUrl = null; + + private static byte[] confVarByteArray = null; + + + private static final Map vars = new HashMap(); + private static final Map metaConfs = new HashMap(); + private final List restrictList = new ArrayList(); + private final Set hiddenSet = new HashSet(); + + private Pattern modWhiteListPattern = null; + private volatile boolean isSparkConfigUpdated = false; + private static final int LOG_PREFIX_LENGTH = 64; + + public boolean getSparkConfigUpdated() { + return isSparkConfigUpdated; + } + + public void setSparkConfigUpdated(boolean isSparkConfigUpdated) { + this.isSparkConfigUpdated = isSparkConfigUpdated; + } + + static { + ClassLoader classLoader = Thread.currentThread().getContextClassLoader(); + if (classLoader == null) { + classLoader = HiveConf.class.getClassLoader(); + } + + hiveDefaultURL = classLoader.getResource("hive-default.xml"); + + // Look for hive-site.xml on the CLASSPATH and log its location if found. + hiveSiteURL = classLoader.getResource("hive-site.xml"); + hivemetastoreSiteUrl = classLoader.getResource("hivemetastore-site.xml"); + hiveServer2SiteUrl = classLoader.getResource("hiveserver2-site.xml"); + + for (ConfVars confVar : ConfVars.values()) { + vars.put(confVar.varname, confVar); + } + } + + /** + * Metastore related options that the db is initialized against. When a conf + * var in this is list is changed, the metastore instance for the CLI will + * be recreated so that the change will take effect. + */ + public static final HiveConf.ConfVars[] metaVars = { + HiveConf.ConfVars.METASTOREWAREHOUSE, + HiveConf.ConfVars.METASTOREURIS, + HiveConf.ConfVars.METASTORE_SERVER_PORT, + HiveConf.ConfVars.METASTORETHRIFTCONNECTIONRETRIES, + HiveConf.ConfVars.METASTORETHRIFTFAILURERETRIES, + HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY, + HiveConf.ConfVars.METASTORE_CLIENT_SOCKET_TIMEOUT, + HiveConf.ConfVars.METASTORE_CLIENT_SOCKET_LIFETIME, + HiveConf.ConfVars.METASTOREPWD, + HiveConf.ConfVars.METASTORECONNECTURLHOOK, + HiveConf.ConfVars.METASTORECONNECTURLKEY, + HiveConf.ConfVars.METASTORESERVERMINTHREADS, + HiveConf.ConfVars.METASTORESERVERMAXTHREADS, + HiveConf.ConfVars.METASTORE_TCP_KEEP_ALIVE, + HiveConf.ConfVars.METASTORE_INT_ORIGINAL, + HiveConf.ConfVars.METASTORE_INT_ARCHIVED, + HiveConf.ConfVars.METASTORE_INT_EXTRACTED, + HiveConf.ConfVars.METASTORE_KERBEROS_KEYTAB_FILE, + HiveConf.ConfVars.METASTORE_KERBEROS_PRINCIPAL, + HiveConf.ConfVars.METASTORE_USE_THRIFT_SASL, + HiveConf.ConfVars.METASTORE_CACHE_PINOBJTYPES, + HiveConf.ConfVars.METASTORE_CONNECTION_POOLING_TYPE, + HiveConf.ConfVars.METASTORE_VALIDATE_TABLES, + HiveConf.ConfVars.METASTORE_VALIDATE_COLUMNS, + HiveConf.ConfVars.METASTORE_VALIDATE_CONSTRAINTS, + HiveConf.ConfVars.METASTORE_STORE_MANAGER_TYPE, + HiveConf.ConfVars.METASTORE_AUTO_CREATE_SCHEMA, + HiveConf.ConfVars.METASTORE_AUTO_START_MECHANISM_MODE, + HiveConf.ConfVars.METASTORE_TRANSACTION_ISOLATION, + HiveConf.ConfVars.METASTORE_CACHE_LEVEL2, + HiveConf.ConfVars.METASTORE_CACHE_LEVEL2_TYPE, + HiveConf.ConfVars.METASTORE_IDENTIFIER_FACTORY, + HiveConf.ConfVars.METASTORE_PLUGIN_REGISTRY_BUNDLE_CHECK, + HiveConf.ConfVars.METASTORE_AUTHORIZATION_STORAGE_AUTH_CHECKS, + HiveConf.ConfVars.METASTORE_BATCH_RETRIEVE_MAX, + HiveConf.ConfVars.METASTORE_EVENT_LISTENERS, + HiveConf.ConfVars.METASTORE_EVENT_CLEAN_FREQ, + HiveConf.ConfVars.METASTORE_EVENT_EXPIRY_DURATION, + HiveConf.ConfVars.METASTORE_FILTER_HOOK, + HiveConf.ConfVars.METASTORE_RAW_STORE_IMPL, + HiveConf.ConfVars.METASTORE_END_FUNCTION_LISTENERS, + HiveConf.ConfVars.METASTORE_PART_INHERIT_TBL_PROPS, + HiveConf.ConfVars.METASTORE_BATCH_RETRIEVE_OBJECTS_MAX, + HiveConf.ConfVars.METASTORE_INIT_HOOKS, + HiveConf.ConfVars.METASTORE_PRE_EVENT_LISTENERS, + HiveConf.ConfVars.HMSHANDLERATTEMPTS, + HiveConf.ConfVars.HMSHANDLERINTERVAL, + HiveConf.ConfVars.HMSHANDLERFORCERELOADCONF, + HiveConf.ConfVars.METASTORE_PARTITION_NAME_WHITELIST_PATTERN, + HiveConf.ConfVars.METASTORE_ORM_RETRIEVE_MAPNULLS_AS_EMPTY_STRINGS, + HiveConf.ConfVars.METASTORE_DISALLOW_INCOMPATIBLE_COL_TYPE_CHANGES, + HiveConf.ConfVars.USERS_IN_ADMIN_ROLE, + HiveConf.ConfVars.HIVE_AUTHORIZATION_MANAGER, + HiveConf.ConfVars.HIVE_TXN_MANAGER, + HiveConf.ConfVars.HIVE_TXN_TIMEOUT, + HiveConf.ConfVars.HIVE_TXN_MAX_OPEN_BATCH, + HiveConf.ConfVars.HIVE_METASTORE_STATS_NDV_DENSITY_FUNCTION, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_ENABLED, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_SIZE, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_PARTITIONS, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_FPP, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_VARIANCE, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_TTL, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_WRITER_WAIT, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_READER_WAIT, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_FULL, + HiveConf.ConfVars.METASTORE_AGGREGATE_STATS_CACHE_CLEAN_UNTIL, + HiveConf.ConfVars.METASTORE_FASTPATH, + HiveConf.ConfVars.METASTORE_HBASE_CATALOG_CACHE_SIZE, + HiveConf.ConfVars.METASTORE_HBASE_AGGREGATE_STATS_CACHE_SIZE, + HiveConf.ConfVars.METASTORE_HBASE_AGGREGATE_STATS_CACHE_MAX_PARTITIONS, + HiveConf.ConfVars.METASTORE_HBASE_AGGREGATE_STATS_CACHE_FALSE_POSITIVE_PROBABILITY, + HiveConf.ConfVars.METASTORE_HBASE_AGGREGATE_STATS_CACHE_MAX_VARIANCE, + HiveConf.ConfVars.METASTORE_HBASE_CACHE_TIME_TO_LIVE, + HiveConf.ConfVars.METASTORE_HBASE_CACHE_MAX_WRITER_WAIT, + HiveConf.ConfVars.METASTORE_HBASE_CACHE_MAX_READER_WAIT, + HiveConf.ConfVars.METASTORE_HBASE_CACHE_MAX_FULL, + HiveConf.ConfVars.METASTORE_HBASE_CACHE_CLEAN_UNTIL, + HiveConf.ConfVars.METASTORE_HBASE_CONNECTION_CLASS, + HiveConf.ConfVars.METASTORE_HBASE_AGGR_STATS_CACHE_ENTRIES, + HiveConf.ConfVars.METASTORE_HBASE_AGGR_STATS_MEMORY_TTL, + HiveConf.ConfVars.METASTORE_HBASE_AGGR_STATS_INVALIDATOR_FREQUENCY, + HiveConf.ConfVars.METASTORE_HBASE_AGGR_STATS_HBASE_TTL + }; + + /** + * User configurable Metastore vars + */ + public static final HiveConf.ConfVars[] metaConfVars = { + HiveConf.ConfVars.METASTORE_TRY_DIRECT_SQL, + HiveConf.ConfVars.METASTORE_TRY_DIRECT_SQL_DDL, + HiveConf.ConfVars.METASTORE_CLIENT_SOCKET_TIMEOUT + }; + + static { + for (ConfVars confVar : metaConfVars) { + metaConfs.put(confVar.varname, confVar); + } + } + + /** + * dbVars are the parameters can be set per database. If these + * parameters are set as a database property, when switching to that + * database, the HiveConf variable will be changed. The change of these + * parameters will effectively change the DFS and MapReduce clusters + * for different databases. + */ + public static final HiveConf.ConfVars[] dbVars = { + HiveConf.ConfVars.HADOOPBIN, + HiveConf.ConfVars.METASTOREWAREHOUSE, + HiveConf.ConfVars.SCRATCHDIR + }; + + /** + * ConfVars. + * + * These are the default configuration properties for Hive. Each HiveConf + * object is initialized as follows: + * + * 1) Hadoop configuration properties are applied. + * 2) ConfVar properties with non-null values are overlayed. + * 3) hive-site.xml properties are overlayed. + * + * WARNING: think twice before adding any Hadoop configuration properties + * with non-null values to this list as they will override any values defined + * in the underlying Hadoop configuration. + */ + public static enum ConfVars { + // QL execution stuff + SCRIPTWRAPPER("hive.exec.script.wrapper", null, ""), + PLAN("hive.exec.plan", "", ""), + PLAN_SERIALIZATION("hive.plan.serialization.format", "kryo", + "Query plan format serialization between client and task nodes. \n" + + "Two supported values are : kryo and javaXML. Kryo is default."), + STAGINGDIR("hive.exec.stagingdir", ".hive-staging", + "Directory name that will be created inside table locations in order to support HDFS encryption. " + + "This is replaces ${hive.exec.scratchdir} for query results with the exception of read-only tables. " + + "In all cases ${hive.exec.scratchdir} is still used for other temporary files, such as job plans."), + SCRATCHDIR("hive.exec.scratchdir", "/tmp/hive", + "HDFS root scratch dir for Hive jobs which gets created with write all (733) permission. " + + "For each connecting user, an HDFS scratch dir: ${hive.exec.scratchdir}/ is created, " + + "with ${hive.scratch.dir.permission}."), + LOCALSCRATCHDIR("hive.exec.local.scratchdir", + "${system:java.io.tmpdir}" + File.separator + "${system:user.name}", + "Local scratch space for Hive jobs"), + DOWNLOADED_RESOURCES_DIR("hive.downloaded.resources.dir", + "${system:java.io.tmpdir}" + File.separator + "${hive.session.id}_resources", + "Temporary local directory for added resources in the remote file system."), + SCRATCHDIRPERMISSION("hive.scratch.dir.permission", "700", + "The permission for the user specific scratch directories that get created."), + SUBMITVIACHILD("hive.exec.submitviachild", false, ""), + SUBMITLOCALTASKVIACHILD("hive.exec.submit.local.task.via.child", true, + "Determines whether local tasks (typically mapjoin hashtable generation phase) runs in \n" + + "separate JVM (true recommended) or not. \n" + + "Avoids the overhead of spawning new JVM, but can lead to out-of-memory issues."), + SCRIPTERRORLIMIT("hive.exec.script.maxerrsize", 100000, + "Maximum number of bytes a script is allowed to emit to standard error (per map-reduce task). \n" + + "This prevents runaway scripts from filling logs partitions to capacity"), + ALLOWPARTIALCONSUMP("hive.exec.script.allow.partial.consumption", false, + "When enabled, this option allows a user script to exit successfully without consuming \n" + + "all the data from the standard input."), + STREAMREPORTERPERFIX("stream.stderr.reporter.prefix", "reporter:", + "Streaming jobs that log to standard error with this prefix can log counter or status information."), + STREAMREPORTERENABLED("stream.stderr.reporter.enabled", true, + "Enable consumption of status and counter messages for streaming jobs."), + COMPRESSRESULT("hive.exec.compress.output", false, + "This controls whether the final outputs of a query (to a local/HDFS file or a Hive table) is compressed. \n" + + "The compression codec and other options are determined from Hadoop config variables mapred.output.compress*"), + COMPRESSINTERMEDIATE("hive.exec.compress.intermediate", false, + "This controls whether intermediate files produced by Hive between multiple map-reduce jobs are compressed. \n" + + "The compression codec and other options are determined from Hadoop config variables mapred.output.compress*"), + COMPRESSINTERMEDIATECODEC("hive.intermediate.compression.codec", "", ""), + COMPRESSINTERMEDIATETYPE("hive.intermediate.compression.type", "", ""), + BYTESPERREDUCER("hive.exec.reducers.bytes.per.reducer", (long) (256 * 1000 * 1000), + "size per reducer.The default is 256Mb, i.e if the input size is 1G, it will use 4 reducers."), + MAXREDUCERS("hive.exec.reducers.max", 1009, + "max number of reducers will be used. If the one specified in the configuration parameter mapred.reduce.tasks is\n" + + "negative, Hive will use this one as the max number of reducers when automatically determine number of reducers."), + PREEXECHOOKS("hive.exec.pre.hooks", "", + "Comma-separated list of pre-execution hooks to be invoked for each statement. \n" + + "A pre-execution hook is specified as the name of a Java class which implements the \n" + + "org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface."), + POSTEXECHOOKS("hive.exec.post.hooks", "", + "Comma-separated list of post-execution hooks to be invoked for each statement. \n" + + "A post-execution hook is specified as the name of a Java class which implements the \n" + + "org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface."), + ONFAILUREHOOKS("hive.exec.failure.hooks", "", + "Comma-separated list of on-failure hooks to be invoked for each statement. \n" + + "An on-failure hook is specified as the name of Java class which implements the \n" + + "org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface."), + QUERYREDACTORHOOKS("hive.exec.query.redactor.hooks", "", + "Comma-separated list of hooks to be invoked for each query which can \n" + + "tranform the query before it's placed in the job.xml file. Must be a Java class which \n" + + "extends from the org.apache.hadoop.hive.ql.hooks.Redactor abstract class."), + CLIENTSTATSPUBLISHERS("hive.client.stats.publishers", "", + "Comma-separated list of statistics publishers to be invoked on counters on each job. \n" + + "A client stats publisher is specified as the name of a Java class which implements the \n" + + "org.apache.hadoop.hive.ql.stats.ClientStatsPublisher interface."), + EXECPARALLEL("hive.exec.parallel", false, "Whether to execute jobs in parallel"), + EXECPARALLETHREADNUMBER("hive.exec.parallel.thread.number", 8, + "How many jobs at most can be executed in parallel"), + HIVESPECULATIVEEXECREDUCERS("hive.mapred.reduce.tasks.speculative.execution", true, + "Whether speculative execution for reducers should be turned on. "), + HIVECOUNTERSPULLINTERVAL("hive.exec.counters.pull.interval", 1000L, + "The interval with which to poll the JobTracker for the counters the running job. \n" + + "The smaller it is the more load there will be on the jobtracker, the higher it is the less granular the caught will be."), + DYNAMICPARTITIONING("hive.exec.dynamic.partition", true, + "Whether or not to allow dynamic partitions in DML/DDL."), + DYNAMICPARTITIONINGMODE("hive.exec.dynamic.partition.mode", "strict", + "In strict mode, the user must specify at least one static partition\n" + + "in case the user accidentally overwrites all partitions.\n" + + "In nonstrict mode all partitions are allowed to be dynamic."), + DYNAMICPARTITIONMAXPARTS("hive.exec.max.dynamic.partitions", 1000, + "Maximum number of dynamic partitions allowed to be created in total."), + DYNAMICPARTITIONMAXPARTSPERNODE("hive.exec.max.dynamic.partitions.pernode", 100, + "Maximum number of dynamic partitions allowed to be created in each mapper/reducer node."), + MAXCREATEDFILES("hive.exec.max.created.files", 100000L, + "Maximum number of HDFS files created by all mappers/reducers in a MapReduce job."), + DEFAULTPARTITIONNAME("hive.exec.default.partition.name", "__HIVE_DEFAULT_PARTITION__", + "The default partition name in case the dynamic partition column value is null/empty string or any other values that cannot be escaped. \n" + + "This value must not contain any special character used in HDFS URI (e.g., ':', '%', '/' etc). \n" + + "The user has to be aware that the dynamic partition value should not contain this value to avoid confusions."), + DEFAULT_ZOOKEEPER_PARTITION_NAME("hive.lockmgr.zookeeper.default.partition.name", "__HIVE_DEFAULT_ZOOKEEPER_PARTITION__", ""), + + // Whether to show a link to the most failed task + debugging tips + SHOW_JOB_FAIL_DEBUG_INFO("hive.exec.show.job.failure.debug.info", true, + "If a job fails, whether to provide a link in the CLI to the task with the\n" + + "most failures, along with debugging hints if applicable."), + JOB_DEBUG_CAPTURE_STACKTRACES("hive.exec.job.debug.capture.stacktraces", true, + "Whether or not stack traces parsed from the task logs of a sampled failed task \n" + + "for each failed job should be stored in the SessionState"), + JOB_DEBUG_TIMEOUT("hive.exec.job.debug.timeout", 30000, ""), + TASKLOG_DEBUG_TIMEOUT("hive.exec.tasklog.debug.timeout", 20000, ""), + OUTPUT_FILE_EXTENSION("hive.output.file.extension", null, + "String used as a file extension for output files. \n" + + "If not set, defaults to the codec extension for text files (e.g. \".gz\"), or no extension otherwise."), + + HIVE_IN_TEST("hive.in.test", false, "internal usage only, true in test mode", true), + + HIVE_IN_TEZ_TEST("hive.in.tez.test", false, "internal use only, true when in testing tez", + true), + + LOCALMODEAUTO("hive.exec.mode.local.auto", false, + "Let Hive determine whether to run in local mode automatically"), + LOCALMODEMAXBYTES("hive.exec.mode.local.auto.inputbytes.max", 134217728L, + "When hive.exec.mode.local.auto is true, input bytes should less than this for local mode."), + LOCALMODEMAXINPUTFILES("hive.exec.mode.local.auto.input.files.max", 4, + "When hive.exec.mode.local.auto is true, the number of tasks should less than this for local mode."), + + DROPIGNORESNONEXISTENT("hive.exec.drop.ignorenonexistent", true, + "Do not report an error if DROP TABLE/VIEW/Index/Function specifies a non-existent table/view/index/function"), + + HIVEIGNOREMAPJOINHINT("hive.ignore.mapjoin.hint", true, "Ignore the mapjoin hint"), + + HIVE_FILE_MAX_FOOTER("hive.file.max.footer", 100, + "maximum number of lines for footer user can define for a table file"), + + HIVE_RESULTSET_USE_UNIQUE_COLUMN_NAMES("hive.resultset.use.unique.column.names", true, + "Make column names unique in the result set by qualifying column names with table alias if needed.\n" + + "Table alias will be added to column names for queries of type \"select *\" or \n" + + "if query explicitly uses table alias \"select r1.x..\"."), + + // Hadoop Configuration Properties + // Properties with null values are ignored and exist only for the purpose of giving us + // a symbolic name to reference in the Hive source code. Properties with non-null + // values will override any values set in the underlying Hadoop configuration. + HADOOPBIN("hadoop.bin.path", findHadoopBinary(), "", true), + HIVE_FS_HAR_IMPL("fs.har.impl", "org.apache.hadoop.hive.shims.HiveHarFileSystem", + "The implementation for accessing Hadoop Archives. Note that this won't be applicable to Hadoop versions less than 0.20"), + MAPREDMAXSPLITSIZE(FileInputFormat.SPLIT_MAXSIZE, 256000000L, "", true), + MAPREDMINSPLITSIZE(FileInputFormat.SPLIT_MINSIZE, 1L, "", true), + MAPREDMINSPLITSIZEPERNODE(CombineFileInputFormat.SPLIT_MINSIZE_PERNODE, 1L, "", true), + MAPREDMINSPLITSIZEPERRACK(CombineFileInputFormat.SPLIT_MINSIZE_PERRACK, 1L, "", true), + // The number of reduce tasks per job. Hadoop sets this value to 1 by default + // By setting this property to -1, Hive will automatically determine the correct + // number of reducers. + HADOOPNUMREDUCERS("mapreduce.job.reduces", -1, "", true), + + // Metastore stuff. Be sure to update HiveConf.metaVars when you add something here! + METASTOREWAREHOUSE("hive.metastore.warehouse.dir", "/user/hive/warehouse", + "location of default database for the warehouse"), + METASTOREURIS("hive.metastore.uris", "", + "Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore."), + + METASTORE_FASTPATH("hive.metastore.fastpath", false, + "Used to avoid all of the proxies and object copies in the metastore. Note, if this is " + + "set, you MUST use a local metastore (hive.metastore.uris must be empty) otherwise " + + "undefined and most likely undesired behavior will result"), + METASTORE_HBASE_CATALOG_CACHE_SIZE("hive.metastore.hbase.catalog.cache.size", 50000, "Maximum number of " + + "objects we will place in the hbase metastore catalog cache. The objects will be divided up by " + + "types that we need to cache."), + METASTORE_HBASE_AGGREGATE_STATS_CACHE_SIZE("hive.metastore.hbase.aggregate.stats.cache.size", 10000, + "Maximum number of aggregate stats nodes that we will place in the hbase metastore aggregate stats cache."), + METASTORE_HBASE_AGGREGATE_STATS_CACHE_MAX_PARTITIONS("hive.metastore.hbase.aggregate.stats.max.partitions", 10000, + "Maximum number of partitions that are aggregated per cache node."), + METASTORE_HBASE_AGGREGATE_STATS_CACHE_FALSE_POSITIVE_PROBABILITY("hive.metastore.hbase.aggregate.stats.false.positive.probability", + (float) 0.01, "Maximum false positive probability for the Bloom Filter used in each aggregate stats cache node (default 1%)."), + METASTORE_HBASE_AGGREGATE_STATS_CACHE_MAX_VARIANCE("hive.metastore.hbase.aggregate.stats.max.variance", (float) 0.1, + "Maximum tolerable variance in number of partitions between a cached node and our request (default 10%)."), + METASTORE_HBASE_CACHE_TIME_TO_LIVE("hive.metastore.hbase.cache.ttl", "600s", new TimeValidator(TimeUnit.SECONDS), + "Number of seconds for a cached node to be active in the cache before they become stale."), + METASTORE_HBASE_CACHE_MAX_WRITER_WAIT("hive.metastore.hbase.cache.max.writer.wait", "5000ms", new TimeValidator(TimeUnit.MILLISECONDS), + "Number of milliseconds a writer will wait to acquire the writelock before giving up."), + METASTORE_HBASE_CACHE_MAX_READER_WAIT("hive.metastore.hbase.cache.max.reader.wait", "1000ms", new TimeValidator(TimeUnit.MILLISECONDS), + "Number of milliseconds a reader will wait to acquire the readlock before giving up."), + METASTORE_HBASE_CACHE_MAX_FULL("hive.metastore.hbase.cache.max.full", (float) 0.9, + "Maximum cache full % after which the cache cleaner thread kicks in."), + METASTORE_HBASE_CACHE_CLEAN_UNTIL("hive.metastore.hbase.cache.clean.until", (float) 0.8, + "The cleaner thread cleans until cache reaches this % full size."), + METASTORE_HBASE_CONNECTION_CLASS("hive.metastore.hbase.connection.class", + "org.apache.hadoop.hive.metastore.hbase.VanillaHBaseConnection", + "Class used to connection to HBase"), + METASTORE_HBASE_AGGR_STATS_CACHE_ENTRIES("hive.metastore.hbase.aggr.stats.cache.entries", + 10000, "How many in stats objects to cache in memory"), + METASTORE_HBASE_AGGR_STATS_MEMORY_TTL("hive.metastore.hbase.aggr.stats.memory.ttl", "60s", + new TimeValidator(TimeUnit.SECONDS), + "Number of seconds stats objects live in memory after they are read from HBase."), + METASTORE_HBASE_AGGR_STATS_INVALIDATOR_FREQUENCY( + "hive.metastore.hbase.aggr.stats.invalidator.frequency", "5s", + new TimeValidator(TimeUnit.SECONDS), + "How often the stats cache scans its HBase entries and looks for expired entries"), + METASTORE_HBASE_AGGR_STATS_HBASE_TTL("hive.metastore.hbase.aggr.stats.hbase.ttl", "604800s", + new TimeValidator(TimeUnit.SECONDS), + "Number of seconds stats entries live in HBase cache after they are created. They may be" + + " invalided by updates or partition drops before this. Default is one week."), + + METASTORETHRIFTCONNECTIONRETRIES("hive.metastore.connect.retries", 3, + "Number of retries while opening a connection to metastore"), + METASTORETHRIFTFAILURERETRIES("hive.metastore.failure.retries", 1, + "Number of retries upon failure of Thrift metastore calls"), + METASTORE_SERVER_PORT("hive.metastore.port", 9083, "Hive metastore listener port"), + METASTORE_CLIENT_CONNECT_RETRY_DELAY("hive.metastore.client.connect.retry.delay", "1s", + new TimeValidator(TimeUnit.SECONDS), + "Number of seconds for the client to wait between consecutive connection attempts"), + METASTORE_CLIENT_SOCKET_TIMEOUT("hive.metastore.client.socket.timeout", "600s", + new TimeValidator(TimeUnit.SECONDS), + "MetaStore Client socket timeout in seconds"), + METASTORE_CLIENT_SOCKET_LIFETIME("hive.metastore.client.socket.lifetime", "0s", + new TimeValidator(TimeUnit.SECONDS), + "MetaStore Client socket lifetime in seconds. After this time is exceeded, client\n" + + "reconnects on the next MetaStore operation. A value of 0s means the connection\n" + + "has an infinite lifetime."), + METASTOREPWD("javax.jdo.option.ConnectionPassword", "mine", + "password to use against metastore database"), + METASTORECONNECTURLHOOK("hive.metastore.ds.connection.url.hook", "", + "Name of the hook to use for retrieving the JDO connection URL. If empty, the value in javax.jdo.option.ConnectionURL is used"), + METASTOREMULTITHREADED("javax.jdo.option.Multithreaded", true, + "Set this to true if multiple threads access metastore through JDO concurrently."), + METASTORECONNECTURLKEY("javax.jdo.option.ConnectionURL", + "jdbc:derby:;databaseName=metastore_db;create=true", + "JDBC connect string for a JDBC metastore"), + HMSHANDLERATTEMPTS("hive.hmshandler.retry.attempts", 10, + "The number of times to retry a HMSHandler call if there were a connection error."), + HMSHANDLERINTERVAL("hive.hmshandler.retry.interval", "2000ms", + new TimeValidator(TimeUnit.MILLISECONDS), "The time between HMSHandler retry attempts on failure."), + HMSHANDLERFORCERELOADCONF("hive.hmshandler.force.reload.conf", false, + "Whether to force reloading of the HMSHandler configuration (including\n" + + "the connection URL, before the next metastore query that accesses the\n" + + "datastore. Once reloaded, this value is reset to false. Used for\n" + + "testing only."), + METASTORESERVERMAXMESSAGESIZE("hive.metastore.server.max.message.size", 100*1024*1024, + "Maximum message size in bytes a HMS will accept."), + METASTORESERVERMINTHREADS("hive.metastore.server.min.threads", 200, + "Minimum number of worker threads in the Thrift server's pool."), + METASTORESERVERMAXTHREADS("hive.metastore.server.max.threads", 1000, + "Maximum number of worker threads in the Thrift server's pool."), + METASTORE_TCP_KEEP_ALIVE("hive.metastore.server.tcp.keepalive", true, + "Whether to enable TCP keepalive for the metastore server. Keepalive will prevent accumulation of half-open connections."), + + METASTORE_INT_ORIGINAL("hive.metastore.archive.intermediate.original", + "_INTERMEDIATE_ORIGINAL", + "Intermediate dir suffixes used for archiving. Not important what they\n" + + "are, as long as collisions are avoided"), + METASTORE_INT_ARCHIVED("hive.metastore.archive.intermediate.archived", + "_INTERMEDIATE_ARCHIVED", ""), + METASTORE_INT_EXTRACTED("hive.metastore.archive.intermediate.extracted", + "_INTERMEDIATE_EXTRACTED", ""), + METASTORE_KERBEROS_KEYTAB_FILE("hive.metastore.kerberos.keytab.file", "", + "The path to the Kerberos Keytab file containing the metastore Thrift server's service principal."), + METASTORE_KERBEROS_PRINCIPAL("hive.metastore.kerberos.principal", + "hive-metastore/_HOST@EXAMPLE.COM", + "The service principal for the metastore Thrift server. \n" + + "The special string _HOST will be replaced automatically with the correct host name."), + METASTORE_USE_THRIFT_SASL("hive.metastore.sasl.enabled", false, + "If true, the metastore Thrift interface will be secured with SASL. Clients must authenticate with Kerberos."), + METASTORE_USE_THRIFT_FRAMED_TRANSPORT("hive.metastore.thrift.framed.transport.enabled", false, + "If true, the metastore Thrift interface will use TFramedTransport. When false (default) a standard TTransport is used."), + METASTORE_USE_THRIFT_COMPACT_PROTOCOL("hive.metastore.thrift.compact.protocol.enabled", false, + "If true, the metastore Thrift interface will use TCompactProtocol. When false (default) TBinaryProtocol will be used.\n" + + "Setting it to true will break compatibility with older clients running TBinaryProtocol."), + METASTORE_CLUSTER_DELEGATION_TOKEN_STORE_CLS("hive.cluster.delegation.token.store.class", + "org.apache.hadoop.hive.thrift.MemoryTokenStore", + "The delegation token store implementation. Set to org.apache.hadoop.hive.thrift.ZooKeeperTokenStore for load-balanced cluster."), + METASTORE_CLUSTER_DELEGATION_TOKEN_STORE_ZK_CONNECTSTR( + "hive.cluster.delegation.token.store.zookeeper.connectString", "", + "The ZooKeeper token store connect string. You can re-use the configuration value\n" + + "set in hive.zookeeper.quorum, by leaving this parameter unset."), + METASTORE_CLUSTER_DELEGATION_TOKEN_STORE_ZK_ZNODE( + "hive.cluster.delegation.token.store.zookeeper.znode", "/hivedelegation", + "The root path for token store data. Note that this is used by both HiveServer2 and\n" + + "MetaStore to store delegation Token. One directory gets created for each of them.\n" + + "The final directory names would have the servername appended to it (HIVESERVER2,\n" + + "METASTORE)."), + METASTORE_CLUSTER_DELEGATION_TOKEN_STORE_ZK_ACL( + "hive.cluster.delegation.token.store.zookeeper.acl", "", + "ACL for token store entries. Comma separated list of ACL entries. For example:\n" + + "sasl:hive/host1@MY.DOMAIN:cdrwa,sasl:hive/host2@MY.DOMAIN:cdrwa\n" + + "Defaults to all permissions for the hiveserver2/metastore process user."), + METASTORE_CACHE_PINOBJTYPES("hive.metastore.cache.pinobjtypes", "Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order", + "List of comma separated metastore object types that should be pinned in the cache"), + METASTORE_CONNECTION_POOLING_TYPE("datanucleus.connectionPoolingType", "BONECP", + "Specify connection pool library for datanucleus"), + METASTORE_VALIDATE_TABLES("datanucleus.validateTables", false, + "validates existing schema against code. turn this on if you want to verify existing schema"), + METASTORE_VALIDATE_COLUMNS("datanucleus.validateColumns", false, + "validates existing schema against code. turn this on if you want to verify existing schema"), + METASTORE_VALIDATE_CONSTRAINTS("datanucleus.validateConstraints", false, + "validates existing schema against code. turn this on if you want to verify existing schema"), + METASTORE_STORE_MANAGER_TYPE("datanucleus.storeManagerType", "rdbms", "metadata store type"), + METASTORE_AUTO_CREATE_SCHEMA("datanucleus.autoCreateSchema", false, + "creates necessary schema on a startup if one doesn't exist. set this to false, after creating it once"), + METASTORE_FIXED_DATASTORE("datanucleus.fixedDatastore", true, "Dictates whether to allow updates to schema or not."), + METASTORE_SCHEMA_VERIFICATION("hive.metastore.schema.verification", true, + "Enforce metastore schema version consistency.\n" + + "True: Verify that version information stored in metastore matches with one from Hive jars. Also disable automatic\n" + + " schema migration attempt. Users are required to manually migrate schema after Hive upgrade which ensures\n" + + " proper metastore schema migration. (Default)\n" + + "False: Warn if the version information stored in metastore doesn't match with one from in Hive jars."), + METASTORE_SCHEMA_VERIFICATION_RECORD_VERSION("hive.metastore.schema.verification.record.version", true, + "When true the current MS version is recorded in the VERSION table. If this is disabled and verification is\n" + + " enabled the MS will be unusable."), + METASTORE_AUTO_START_MECHANISM_MODE("datanucleus.autoStartMechanismMode", "checked", + "throw exception if metadata tables are incorrect"), + METASTORE_TRANSACTION_ISOLATION("datanucleus.transactionIsolation", "read-committed", + "Default transaction isolation level for identity generation."), + METASTORE_CACHE_LEVEL2("datanucleus.cache.level2", false, + "Use a level 2 cache. Turn this off if metadata is changed independently of Hive metastore server"), + METASTORE_CACHE_LEVEL2_TYPE("datanucleus.cache.level2.type", "none", ""), + METASTORE_IDENTIFIER_FACTORY("datanucleus.identifierFactory", "datanucleus1", + "Name of the identifier factory to use when generating table/column names etc. \n" + + "'datanucleus1' is used for backward compatibility with DataNucleus v1"), + METASTORE_USE_LEGACY_VALUE_STRATEGY("datanucleus.rdbms.useLegacyNativeValueStrategy", true, ""), + METASTORE_PLUGIN_REGISTRY_BUNDLE_CHECK("datanucleus.plugin.pluginRegistryBundleCheck", "LOG", + "Defines what happens when plugin bundles are found and are duplicated [EXCEPTION|LOG|NONE]"), + METASTORE_BATCH_RETRIEVE_MAX("hive.metastore.batch.retrieve.max", 300, + "Maximum number of objects (tables/partitions) can be retrieved from metastore in one batch. \n" + + "The higher the number, the less the number of round trips is needed to the Hive metastore server, \n" + + "but it may also cause higher memory requirement at the client side."), + METASTORE_BATCH_RETRIEVE_OBJECTS_MAX( + "hive.metastore.batch.retrieve.table.partition.max", 1000, + "Maximum number of objects that metastore internally retrieves in one batch."), + + METASTORE_INIT_HOOKS("hive.metastore.init.hooks", "", + "A comma separated list of hooks to be invoked at the beginning of HMSHandler initialization. \n" + + "An init hook is specified as the name of Java class which extends org.apache.hadoop.hive.metastore.MetaStoreInitListener."), + METASTORE_PRE_EVENT_LISTENERS("hive.metastore.pre.event.listeners", "", + "List of comma separated listeners for metastore events."), + METASTORE_EVENT_LISTENERS("hive.metastore.event.listeners", "", ""), + METASTORE_EVENT_DB_LISTENER_TTL("hive.metastore.event.db.listener.timetolive", "86400s", + new TimeValidator(TimeUnit.SECONDS), + "time after which events will be removed from the database listener queue"), + METASTORE_AUTHORIZATION_STORAGE_AUTH_CHECKS("hive.metastore.authorization.storage.checks", false, + "Should the metastore do authorization checks against the underlying storage (usually hdfs) \n" + + "for operations like drop-partition (disallow the drop-partition if the user in\n" + + "question doesn't have permissions to delete the corresponding directory\n" + + "on the storage)."), + METASTORE_EVENT_CLEAN_FREQ("hive.metastore.event.clean.freq", "0s", + new TimeValidator(TimeUnit.SECONDS), + "Frequency at which timer task runs to purge expired events in metastore."), + METASTORE_EVENT_EXPIRY_DURATION("hive.metastore.event.expiry.duration", "0s", + new TimeValidator(TimeUnit.SECONDS), + "Duration after which events expire from events table"), + METASTORE_EXECUTE_SET_UGI("hive.metastore.execute.setugi", true, + "In unsecure mode, setting this property to true will cause the metastore to execute DFS operations using \n" + + "the client's reported user and group permissions. Note that this property must be set on \n" + + "both the client and server sides. Further note that its best effort. \n" + + "If client sets its to true and server sets it to false, client setting will be ignored."), + METASTORE_PARTITION_NAME_WHITELIST_PATTERN("hive.metastore.partition.name.whitelist.pattern", "", + "Partition names will be checked against this regex pattern and rejected if not matched."), + + METASTORE_INTEGER_JDO_PUSHDOWN("hive.metastore.integral.jdo.pushdown", false, + "Allow JDO query pushdown for integral partition columns in metastore. Off by default. This\n" + + "improves metastore perf for integral columns, especially if there's a large number of partitions.\n" + + "However, it doesn't work correctly with integral values that are not normalized (e.g. have\n" + + "leading zeroes, like 0012). If metastore direct SQL is enabled and works, this optimization\n" + + "is also irrelevant."), + METASTORE_TRY_DIRECT_SQL("hive.metastore.try.direct.sql", true, + "Whether the Hive metastore should try to use direct SQL queries instead of the\n" + + "DataNucleus for certain read paths. This can improve metastore performance when\n" + + "fetching many partitions or column statistics by orders of magnitude; however, it\n" + + "is not guaranteed to work on all RDBMS-es and all versions. In case of SQL failures,\n" + + "the metastore will fall back to the DataNucleus, so it's safe even if SQL doesn't\n" + + "work for all queries on your datastore. If all SQL queries fail (for example, your\n" + + "metastore is backed by MongoDB), you might want to disable this to save the\n" + + "try-and-fall-back cost."), + METASTORE_DIRECT_SQL_PARTITION_BATCH_SIZE("hive.metastore.direct.sql.batch.size", 0, + "Batch size for partition and other object retrieval from the underlying DB in direct\n" + + "SQL. For some DBs like Oracle and MSSQL, there are hardcoded or perf-based limitations\n" + + "that necessitate this. For DBs that can handle the queries, this isn't necessary and\n" + + "may impede performance. -1 means no batching, 0 means automatic batching."), + METASTORE_TRY_DIRECT_SQL_DDL("hive.metastore.try.direct.sql.ddl", true, + "Same as hive.metastore.try.direct.sql, for read statements within a transaction that\n" + + "modifies metastore data. Due to non-standard behavior in Postgres, if a direct SQL\n" + + "select query has incorrect syntax or something similar inside a transaction, the\n" + + "entire transaction will fail and fall-back to DataNucleus will not be possible. You\n" + + "should disable the usage of direct SQL inside transactions if that happens in your case."), + METASTORE_ORM_RETRIEVE_MAPNULLS_AS_EMPTY_STRINGS("hive.metastore.orm.retrieveMapNullsAsEmptyStrings",false, + "Thrift does not support nulls in maps, so any nulls present in maps retrieved from ORM must " + + "either be pruned or converted to empty strings. Some backing dbs such as Oracle persist empty strings " + + "as nulls, so we should set this parameter if we wish to reverse that behaviour. For others, " + + "pruning is the correct behaviour"), + METASTORE_DISALLOW_INCOMPATIBLE_COL_TYPE_CHANGES( + "hive.metastore.disallow.incompatible.col.type.changes", true, + "If true (default is false), ALTER TABLE operations which change the type of a\n" + + "column (say STRING) to an incompatible type (say MAP) are disallowed.\n" + + "RCFile default SerDe (ColumnarSerDe) serializes the values in such a way that the\n" + + "datatypes can be converted from string to any type. The map is also serialized as\n" + + "a string, which can be read as a string as well. However, with any binary\n" + + "serialization, this is not true. Blocking the ALTER TABLE prevents ClassCastExceptions\n" + + "when subsequently trying to access old partitions.\n" + + "\n" + + "Primitive types like INT, STRING, BIGINT, etc., are compatible with each other and are\n" + + "not blocked.\n" + + "\n" + + "See HIVE-4409 for more details."), + + NEWTABLEDEFAULTPARA("hive.table.parameters.default", "", + "Default property values for newly created tables"), + DDL_CTL_PARAMETERS_WHITELIST("hive.ddl.createtablelike.properties.whitelist", "", + "Table Properties to copy over when executing a Create Table Like."), + METASTORE_RAW_STORE_IMPL("hive.metastore.rawstore.impl", "org.apache.hadoop.hive.metastore.ObjectStore", + "Name of the class that implements org.apache.hadoop.hive.metastore.rawstore interface. \n" + + "This class is used to store and retrieval of raw metadata objects such as table, database"), + METASTORE_CONNECTION_DRIVER("javax.jdo.option.ConnectionDriverName", "org.apache.derby.jdbc.EmbeddedDriver", + "Driver class name for a JDBC metastore"), + METASTORE_MANAGER_FACTORY_CLASS("javax.jdo.PersistenceManagerFactoryClass", + "org.datanucleus.api.jdo.JDOPersistenceManagerFactory", + "class implementing the jdo persistence"), + METASTORE_EXPRESSION_PROXY_CLASS("hive.metastore.expression.proxy", + "org.apache.hadoop.hive.ql.optimizer.ppr.PartitionExpressionForMetastore", ""), + METASTORE_DETACH_ALL_ON_COMMIT("javax.jdo.option.DetachAllOnCommit", true, + "Detaches all objects from session so that they can be used after transaction is committed"), + METASTORE_NON_TRANSACTIONAL_READ("javax.jdo.option.NonTransactionalRead", true, + "Reads outside of transactions"), + METASTORE_CONNECTION_USER_NAME("javax.jdo.option.ConnectionUserName", "APP", + "Username to use against metastore database"), + METASTORE_END_FUNCTION_LISTENERS("hive.metastore.end.function.listeners", "", + "List of comma separated listeners for the end of metastore functions."), + METASTORE_PART_INHERIT_TBL_PROPS("hive.metastore.partition.inherit.table.properties", "", + "List of comma separated keys occurring in table properties which will get inherited to newly created partitions. \n" + + "* implies all the keys will get inherited."), + METASTORE_FILTER_HOOK("hive.metastore.filter.hook", "org.apache.hadoop.hive.metastore.DefaultMetaStoreFilterHookImpl", + "Metastore hook class for filtering the metadata read results. If hive.security.authorization.manager" + + "is set to instance of HiveAuthorizerFactory, then this value is ignored."), + FIRE_EVENTS_FOR_DML("hive.metastore.dml.events", false, "If true, the metastore will be asked" + + " to fire events for DML operations"), + METASTORE_CLIENT_DROP_PARTITIONS_WITH_EXPRESSIONS("hive.metastore.client.drop.partitions.using.expressions", true, + "Choose whether dropping partitions with HCatClient pushes the partition-predicate to the metastore, " + + "or drops partitions iteratively"), + + METASTORE_AGGREGATE_STATS_CACHE_ENABLED("hive.metastore.aggregate.stats.cache.enabled", true, + "Whether aggregate stats caching is enabled or not."), + METASTORE_AGGREGATE_STATS_CACHE_SIZE("hive.metastore.aggregate.stats.cache.size", 10000, + "Maximum number of aggregate stats nodes that we will place in the metastore aggregate stats cache."), + METASTORE_AGGREGATE_STATS_CACHE_MAX_PARTITIONS("hive.metastore.aggregate.stats.cache.max.partitions", 10000, + "Maximum number of partitions that are aggregated per cache node."), + METASTORE_AGGREGATE_STATS_CACHE_FPP("hive.metastore.aggregate.stats.cache.fpp", (float) 0.01, + "Maximum false positive probability for the Bloom Filter used in each aggregate stats cache node (default 1%)."), + METASTORE_AGGREGATE_STATS_CACHE_MAX_VARIANCE("hive.metastore.aggregate.stats.cache.max.variance", (float) 0.01, + "Maximum tolerable variance in number of partitions between a cached node and our request (default 1%)."), + METASTORE_AGGREGATE_STATS_CACHE_TTL("hive.metastore.aggregate.stats.cache.ttl", "600s", new TimeValidator(TimeUnit.SECONDS), + "Number of seconds for a cached node to be active in the cache before they become stale."), + METASTORE_AGGREGATE_STATS_CACHE_MAX_WRITER_WAIT("hive.metastore.aggregate.stats.cache.max.writer.wait", "5000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Number of milliseconds a writer will wait to acquire the writelock before giving up."), + METASTORE_AGGREGATE_STATS_CACHE_MAX_READER_WAIT("hive.metastore.aggregate.stats.cache.max.reader.wait", "1000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Number of milliseconds a reader will wait to acquire the readlock before giving up."), + METASTORE_AGGREGATE_STATS_CACHE_MAX_FULL("hive.metastore.aggregate.stats.cache.max.full", (float) 0.9, + "Maximum cache full % after which the cache cleaner thread kicks in."), + METASTORE_AGGREGATE_STATS_CACHE_CLEAN_UNTIL("hive.metastore.aggregate.stats.cache.clean.until", (float) 0.8, + "The cleaner thread cleans until cache reaches this % full size."), + METASTORE_METRICS("hive.metastore.metrics.enabled", false, "Enable metrics on the metastore."), + + // Parameters for exporting metadata on table drop (requires the use of the) + // org.apache.hadoop.hive.ql.parse.MetaDataExportListener preevent listener + METADATA_EXPORT_LOCATION("hive.metadata.export.location", "", + "When used in conjunction with the org.apache.hadoop.hive.ql.parse.MetaDataExportListener pre event listener, \n" + + "it is the location to which the metadata will be exported. The default is an empty string, which results in the \n" + + "metadata being exported to the current user's home directory on HDFS."), + MOVE_EXPORTED_METADATA_TO_TRASH("hive.metadata.move.exported.metadata.to.trash", true, + "When used in conjunction with the org.apache.hadoop.hive.ql.parse.MetaDataExportListener pre event listener, \n" + + "this setting determines if the metadata that is exported will subsequently be moved to the user's trash directory \n" + + "alongside the dropped table data. This ensures that the metadata will be cleaned up along with the dropped table data."), + + // CLI + CLIIGNOREERRORS("hive.cli.errors.ignore", false, ""), + CLIPRINTCURRENTDB("hive.cli.print.current.db", false, + "Whether to include the current database in the Hive prompt."), + CLIPROMPT("hive.cli.prompt", "hive", + "Command line prompt configuration value. Other hiveconf can be used in this configuration value. \n" + + "Variable substitution will only be invoked at the Hive CLI startup."), + CLIPRETTYOUTPUTNUMCOLS("hive.cli.pretty.output.num.cols", -1, + "The number of columns to use when formatting output generated by the DESCRIBE PRETTY table_name command.\n" + + "If the value of this property is -1, then Hive will use the auto-detected terminal width."), + + HIVE_METASTORE_FS_HANDLER_CLS("hive.metastore.fs.handler.class", "org.apache.hadoop.hive.metastore.HiveMetaStoreFsImpl", ""), + + // Things we log in the jobconf + + // session identifier + HIVESESSIONID("hive.session.id", "", ""), + // whether session is running in silent mode or not + HIVESESSIONSILENT("hive.session.silent", false, ""), + + HIVE_SESSION_HISTORY_ENABLED("hive.session.history.enabled", false, + "Whether to log Hive query, query plan, runtime statistics etc."), + + HIVEQUERYSTRING("hive.query.string", "", + "Query being executed (might be multiple per a session)"), + + HIVEQUERYID("hive.query.id", "", + "ID for query being executed (might be multiple per a session)"), + + HIVEJOBNAMELENGTH("hive.jobname.length", 50, "max jobname length"), + + // hive jar + HIVEJAR("hive.jar.path", "", + "The location of hive_cli.jar that is used when submitting jobs in a separate jvm."), + HIVEAUXJARS("hive.aux.jars.path", "", + "The location of the plugin jars that contain implementations of user defined functions and serdes."), + + // reloadable jars + HIVERELOADABLEJARS("hive.reloadable.aux.jars.path", "", + "Jars can be renewed by executing reload command. And these jars can be " + + "used as the auxiliary classes like creating a UDF or SerDe."), + + // hive added files and jars + HIVEADDEDFILES("hive.added.files.path", "", "This an internal parameter."), + HIVEADDEDJARS("hive.added.jars.path", "", "This an internal parameter."), + HIVEADDEDARCHIVES("hive.added.archives.path", "", "This an internal parameter."), + + HIVE_CURRENT_DATABASE("hive.current.database", "", "Database name used by current session. Internal usage only.", true), + + // for hive script operator + HIVES_AUTO_PROGRESS_TIMEOUT("hive.auto.progress.timeout", "0s", + new TimeValidator(TimeUnit.SECONDS), + "How long to run autoprogressor for the script/UDTF operators.\n" + + "Set to 0 for forever."), + HIVESCRIPTAUTOPROGRESS("hive.script.auto.progress", false, + "Whether Hive Transform/Map/Reduce Clause should automatically send progress information to TaskTracker \n" + + "to avoid the task getting killed because of inactivity. Hive sends progress information when the script is \n" + + "outputting to stderr. This option removes the need of periodically producing stderr messages, \n" + + "but users should be cautious because this may prevent infinite loops in the scripts to be killed by TaskTracker."), + HIVESCRIPTIDENVVAR("hive.script.operator.id.env.var", "HIVE_SCRIPT_OPERATOR_ID", + "Name of the environment variable that holds the unique script operator ID in the user's \n" + + "transform function (the custom mapper/reducer that the user has specified in the query)"), + HIVESCRIPTTRUNCATEENV("hive.script.operator.truncate.env", false, + "Truncate each environment variable for external script in scripts operator to 20KB (to fit system limits)"), + HIVESCRIPT_ENV_BLACKLIST("hive.script.operator.env.blacklist", + "hive.txn.valid.txns,hive.script.operator.env.blacklist", + "Comma separated list of keys from the configuration file not to convert to environment " + + "variables when envoking the script operator"), + HIVEMAPREDMODE("hive.mapred.mode", "nonstrict", + "The mode in which the Hive operations are being performed. \n" + + "In strict mode, some risky queries are not allowed to run. They include:\n" + + " Cartesian Product.\n" + + " No partition being picked up for a query.\n" + + " Comparing bigints and strings.\n" + + " Comparing bigints and doubles.\n" + + " Orderby without limit."), + HIVEALIAS("hive.alias", "", ""), + HIVEMAPSIDEAGGREGATE("hive.map.aggr", true, "Whether to use map-side aggregation in Hive Group By queries"), + HIVEGROUPBYSKEW("hive.groupby.skewindata", false, "Whether there is skew in data to optimize group by queries"), + HIVEJOINEMITINTERVAL("hive.join.emit.interval", 1000, + "How many rows in the right-most join operand Hive should buffer before emitting the join result."), + HIVEJOINCACHESIZE("hive.join.cache.size", 25000, + "How many rows in the joining tables (except the streaming table) should be cached in memory."), + + // CBO related + HIVE_CBO_ENABLED("hive.cbo.enable", true, "Flag to control enabling Cost Based Optimizations using Calcite framework."), + HIVE_CBO_RETPATH_HIVEOP("hive.cbo.returnpath.hiveop", false, "Flag to control calcite plan to hive operator conversion"), + HIVE_CBO_EXTENDED_COST_MODEL("hive.cbo.costmodel.extended", false, "Flag to control enabling the extended cost model based on" + + "CPU, IO and cardinality. Otherwise, the cost model is based on cardinality."), + HIVE_CBO_COST_MODEL_CPU("hive.cbo.costmodel.cpu", "0.000001", "Default cost of a comparison"), + HIVE_CBO_COST_MODEL_NET("hive.cbo.costmodel.network", "150.0", "Default cost of a transfering a byte over network;" + + " expressed as multiple of CPU cost"), + HIVE_CBO_COST_MODEL_LFS_WRITE("hive.cbo.costmodel.local.fs.write", "4.0", "Default cost of writing a byte to local FS;" + + " expressed as multiple of NETWORK cost"), + HIVE_CBO_COST_MODEL_LFS_READ("hive.cbo.costmodel.local.fs.read", "4.0", "Default cost of reading a byte from local FS;" + + " expressed as multiple of NETWORK cost"), + HIVE_CBO_COST_MODEL_HDFS_WRITE("hive.cbo.costmodel.hdfs.write", "10.0", "Default cost of writing a byte to HDFS;" + + " expressed as multiple of Local FS write cost"), + HIVE_CBO_COST_MODEL_HDFS_READ("hive.cbo.costmodel.hdfs.read", "1.5", "Default cost of reading a byte from HDFS;" + + " expressed as multiple of Local FS read cost"), + AGGR_JOIN_TRANSPOSE("hive.transpose.aggr.join", false, "push aggregates through join"), + + // hive.mapjoin.bucket.cache.size has been replaced by hive.smbjoin.cache.row, + // need to remove by hive .13. Also, do not change default (see SMB operator) + HIVEMAPJOINBUCKETCACHESIZE("hive.mapjoin.bucket.cache.size", 100, ""), + + HIVEMAPJOINUSEOPTIMIZEDTABLE("hive.mapjoin.optimized.hashtable", true, + "Whether Hive should use memory-optimized hash table for MapJoin.\n" + + "Only works on Tez and Spark, because memory-optimized hashtable cannot be serialized."), + HIVEMAPJOINOPTIMIZEDTABLEPROBEPERCENT("hive.mapjoin.optimized.hashtable.probe.percent", + (float) 0.5, "Probing space percentage of the optimized hashtable"), + HIVEUSEHYBRIDGRACEHASHJOIN("hive.mapjoin.hybridgrace.hashtable", true, "Whether to use hybrid" + + "grace hash join as the join method for mapjoin. Tez only."), + HIVEHYBRIDGRACEHASHJOINMEMCHECKFREQ("hive.mapjoin.hybridgrace.memcheckfrequency", 1024, "For " + + "hybrid grace hash join, how often (how many rows apart) we check if memory is full. " + + "This number should be power of 2."), + HIVEHYBRIDGRACEHASHJOINMINWBSIZE("hive.mapjoin.hybridgrace.minwbsize", 524288, "For hybrid grace" + + "Hash join, the minimum write buffer size used by optimized hashtable. Default is 512 KB."), + HIVEHYBRIDGRACEHASHJOINMINNUMPARTITIONS("hive.mapjoin.hybridgrace.minnumpartitions", 16, "For" + + "Hybrid grace hash join, the minimum number of partitions to create."), + HIVEHASHTABLEWBSIZE("hive.mapjoin.optimized.hashtable.wbsize", 8 * 1024 * 1024, + "Optimized hashtable (see hive.mapjoin.optimized.hashtable) uses a chain of buffers to\n" + + "store data. This is one buffer size. HT may be slightly faster if this is larger, but for small\n" + + "joins unnecessary memory will be allocated and then trimmed."), + + HIVESMBJOINCACHEROWS("hive.smbjoin.cache.rows", 10000, + "How many rows with the same key value should be cached in memory per smb joined table."), + HIVEGROUPBYMAPINTERVAL("hive.groupby.mapaggr.checkinterval", 100000, + "Number of rows after which size of the grouping keys/aggregation classes is performed"), + HIVEMAPAGGRHASHMEMORY("hive.map.aggr.hash.percentmemory", (float) 0.5, + "Portion of total memory to be used by map-side group aggregation hash table"), + HIVEMAPJOINFOLLOWEDBYMAPAGGRHASHMEMORY("hive.mapjoin.followby.map.aggr.hash.percentmemory", (float) 0.3, + "Portion of total memory to be used by map-side group aggregation hash table, when this group by is followed by map join"), + HIVEMAPAGGRMEMORYTHRESHOLD("hive.map.aggr.hash.force.flush.memory.threshold", (float) 0.9, + "The max memory to be used by map-side group aggregation hash table.\n" + + "If the memory usage is higher than this number, force to flush data"), + HIVEMAPAGGRHASHMINREDUCTION("hive.map.aggr.hash.min.reduction", (float) 0.5, + "Hash aggregation will be turned off if the ratio between hash table size and input rows is bigger than this number. \n" + + "Set to 1 to make sure hash aggregation is never turned off."), + HIVEMULTIGROUPBYSINGLEREDUCER("hive.multigroupby.singlereducer", true, + "Whether to optimize multi group by query to generate single M/R job plan. If the multi group by query has \n" + + "common group by keys, it will be optimized to generate single M/R job."), + HIVE_MAP_GROUPBY_SORT("hive.map.groupby.sorted", true, + "If the bucketing/sorting properties of the table exactly match the grouping key, whether to perform \n" + + "the group by in the mapper by using BucketizedHiveInputFormat. The only downside to this\n" + + "is that it limits the number of mappers to the number of files."), + HIVE_GROUPBY_ORDERBY_POSITION_ALIAS("hive.groupby.orderby.position.alias", false, + "Whether to enable using Column Position Alias in Group By or Order By"), + HIVE_NEW_JOB_GROUPING_SET_CARDINALITY("hive.new.job.grouping.set.cardinality", 30, + "Whether a new map-reduce job should be launched for grouping sets/rollups/cubes.\n" + + "For a query like: select a, b, c, count(1) from T group by a, b, c with rollup;\n" + + "4 rows are created per row: (a, b, c), (a, b, null), (a, null, null), (null, null, null).\n" + + "This can lead to explosion across map-reduce boundary if the cardinality of T is very high,\n" + + "and map-side aggregation does not do a very good job. \n" + + "\n" + + "This parameter decides if Hive should add an additional map-reduce job. If the grouping set\n" + + "cardinality (4 in the example above), is more than this value, a new MR job is added under the\n" + + "assumption that the original group by will reduce the data size."), + + // Max filesize used to do a single copy (after that, distcp is used) + HIVE_EXEC_COPYFILE_MAXSIZE("hive.exec.copyfile.maxsize", 32L * 1024 * 1024 /*32M*/, + "Maximum file size (in Mb) that Hive uses to do single HDFS copies between directories." + + "Distributed copies (distcp) will be used instead for bigger files so that copies can be done faster."), + + // for hive udtf operator + HIVEUDTFAUTOPROGRESS("hive.udtf.auto.progress", false, + "Whether Hive should automatically send progress information to TaskTracker \n" + + "when using UDTF's to prevent the task getting killed because of inactivity. Users should be cautious \n" + + "because this may prevent TaskTracker from killing tasks with infinite loops."), + + HIVEDEFAULTFILEFORMAT("hive.default.fileformat", "TextFile", new StringSet("TextFile", "SequenceFile", "RCfile", "ORC"), + "Default file format for CREATE TABLE statement. Users can explicitly override it by CREATE TABLE ... STORED AS [FORMAT]"), + HIVEDEFAULTMANAGEDFILEFORMAT("hive.default.fileformat.managed", "none", + new StringSet("none", "TextFile", "SequenceFile", "RCfile", "ORC"), + "Default file format for CREATE TABLE statement applied to managed tables only. External tables will be \n" + + "created with format specified by hive.default.fileformat. Leaving this null will result in using hive.default.fileformat \n" + + "for all tables."), + HIVEQUERYRESULTFILEFORMAT("hive.query.result.fileformat", "TextFile", new StringSet("TextFile", "SequenceFile", "RCfile"), + "Default file format for storing result of the query."), + HIVECHECKFILEFORMAT("hive.fileformat.check", true, "Whether to check file format or not when loading data files"), + + // default serde for rcfile + HIVEDEFAULTRCFILESERDE("hive.default.rcfile.serde", + "org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe", + "The default SerDe Hive will use for the RCFile format"), + + HIVEDEFAULTSERDE("hive.default.serde", + "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe", + "The default SerDe Hive will use for storage formats that do not specify a SerDe."), + + SERDESUSINGMETASTOREFORSCHEMA("hive.serdes.using.metastore.for.schema", + "org.apache.hadoop.hive.ql.io.orc.OrcSerde," + + "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe," + + "org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe," + + "org.apache.hadoop.hive.serde2.dynamic_type.DynamicSerDe," + + "org.apache.hadoop.hive.serde2.MetadataTypedColumnsetSerDe," + + "org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe," + + "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe," + + "org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe", + "SerDes retrieving schema from metastore. This is an internal parameter."), + + HIVEHISTORYFILELOC("hive.querylog.location", + "${system:java.io.tmpdir}" + File.separator + "${system:user.name}", + "Location of Hive run time structured log file"), + + HIVE_LOG_INCREMENTAL_PLAN_PROGRESS("hive.querylog.enable.plan.progress", true, + "Whether to log the plan's progress every time a job's progress is checked.\n" + + "These logs are written to the location specified by hive.querylog.location"), + + HIVE_LOG_INCREMENTAL_PLAN_PROGRESS_INTERVAL("hive.querylog.plan.progress.interval", "60000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "The interval to wait between logging the plan's progress.\n" + + "If there is a whole number percentage change in the progress of the mappers or the reducers,\n" + + "the progress is logged regardless of this value.\n" + + "The actual interval will be the ceiling of (this value divided by the value of\n" + + "hive.exec.counters.pull.interval) multiplied by the value of hive.exec.counters.pull.interval\n" + + "I.e. if it is not divide evenly by the value of hive.exec.counters.pull.interval it will be\n" + + "logged less frequently than specified.\n" + + "This only has an effect if hive.querylog.enable.plan.progress is set to true."), + + HIVESCRIPTSERDE("hive.script.serde", "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe", + "The default SerDe for transmitting input data to and reading output data from the user scripts. "), + HIVESCRIPTRECORDREADER("hive.script.recordreader", + "org.apache.hadoop.hive.ql.exec.TextRecordReader", + "The default record reader for reading data from the user scripts. "), + HIVESCRIPTRECORDWRITER("hive.script.recordwriter", + "org.apache.hadoop.hive.ql.exec.TextRecordWriter", + "The default record writer for writing data to the user scripts. "), + HIVESCRIPTESCAPE("hive.transform.escape.input", false, + "This adds an option to escape special chars (newlines, carriage returns and\n" + + "tabs) when they are passed to the user script. This is useful if the Hive tables\n" + + "can contain data that contains special characters."), + HIVEBINARYRECORDMAX("hive.binary.record.max.length", 1000, + "Read from a binary stream and treat each hive.binary.record.max.length bytes as a record. \n" + + "The last record before the end of stream can have less than hive.binary.record.max.length bytes"), + + // HWI + HIVEHWILISTENHOST("hive.hwi.listen.host", "0.0.0.0", "This is the host address the Hive Web Interface will listen on"), + HIVEHWILISTENPORT("hive.hwi.listen.port", "9999", "This is the port the Hive Web Interface will listen on"), + HIVEHWIWARFILE("hive.hwi.war.file", "${env:HWI_WAR_FILE}", + "This sets the path to the HWI war file, relative to ${HIVE_HOME}. "), + + HIVEHADOOPMAXMEM("hive.mapred.local.mem", 0, "mapper/reducer memory in local mode"), + + //small table file size + HIVESMALLTABLESFILESIZE("hive.mapjoin.smalltable.filesize", 25000000L, + "The threshold for the input file size of the small tables; if the file size is smaller \n" + + "than this threshold, it will try to convert the common join into map join"), + + + HIVE_SCHEMA_EVOLUTION("hive.exec.schema.evolution", true, + "Use schema evolution to convert self-describing file format's data to the schema desired by the reader."), + + HIVESAMPLERANDOMNUM("hive.sample.seednumber", 0, + "A number used to percentage sampling. By changing this number, user will change the subsets of data sampled."), + + // test mode in hive mode + HIVETESTMODE("hive.test.mode", false, + "Whether Hive is running in test mode. If yes, it turns on sampling and prefixes the output tablename.", + false), + HIVETESTMODEPREFIX("hive.test.mode.prefix", "test_", + "In test mode, specfies prefixes for the output table", false), + HIVETESTMODESAMPLEFREQ("hive.test.mode.samplefreq", 32, + "In test mode, specfies sampling frequency for table, which is not bucketed,\n" + + "For example, the following query:\n" + + " INSERT OVERWRITE TABLE dest SELECT col1 from src\n" + + "would be converted to\n" + + " INSERT OVERWRITE TABLE test_dest\n" + + " SELECT col1 from src TABLESAMPLE (BUCKET 1 out of 32 on rand(1))", false), + HIVETESTMODENOSAMPLE("hive.test.mode.nosamplelist", "", + "In test mode, specifies comma separated table names which would not apply sampling", false), + HIVETESTMODEDUMMYSTATAGGR("hive.test.dummystats.aggregator", "", "internal variable for test", false), + HIVETESTMODEDUMMYSTATPUB("hive.test.dummystats.publisher", "", "internal variable for test", false), + HIVETESTCURRENTTIMESTAMP("hive.test.currenttimestamp", null, "current timestamp for test", false), + + HIVEMERGEMAPFILES("hive.merge.mapfiles", true, + "Merge small files at the end of a map-only job"), + HIVEMERGEMAPREDFILES("hive.merge.mapredfiles", false, + "Merge small files at the end of a map-reduce job"), + HIVEMERGETEZFILES("hive.merge.tezfiles", false, "Merge small files at the end of a Tez DAG"), + HIVEMERGESPARKFILES("hive.merge.sparkfiles", false, "Merge small files at the end of a Spark DAG Transformation"), + HIVEMERGEMAPFILESSIZE("hive.merge.size.per.task", (long) (256 * 1000 * 1000), + "Size of merged files at the end of the job"), + HIVEMERGEMAPFILESAVGSIZE("hive.merge.smallfiles.avgsize", (long) (16 * 1000 * 1000), + "When the average output file size of a job is less than this number, Hive will start an additional \n" + + "map-reduce job to merge the output files into bigger files. This is only done for map-only jobs \n" + + "if hive.merge.mapfiles is true, and for map-reduce jobs if hive.merge.mapredfiles is true."), + HIVEMERGERCFILEBLOCKLEVEL("hive.merge.rcfile.block.level", true, ""), + HIVEMERGEORCFILESTRIPELEVEL("hive.merge.orcfile.stripe.level", true, + "When hive.merge.mapfiles, hive.merge.mapredfiles or hive.merge.tezfiles is enabled\n" + + "while writing a table with ORC file format, enabling this config will do stripe-level\n" + + "fast merge for small ORC files. Note that enabling this config will not honor the\n" + + "padding tolerance config (hive.exec.orc.block.padding.tolerance)."), + + HIVEUSEEXPLICITRCFILEHEADER("hive.exec.rcfile.use.explicit.header", true, + "If this is set the header for RCFiles will simply be RCF. If this is not\n" + + "set the header will be that borrowed from sequence files, e.g. SEQ- followed\n" + + "by the input and output RCFile formats."), + HIVEUSERCFILESYNCCACHE("hive.exec.rcfile.use.sync.cache", true, ""), + + HIVE_RCFILE_RECORD_INTERVAL("hive.io.rcfile.record.interval", Integer.MAX_VALUE, ""), + HIVE_RCFILE_COLUMN_NUMBER_CONF("hive.io.rcfile.column.number.conf", 0, ""), + HIVE_RCFILE_TOLERATE_CORRUPTIONS("hive.io.rcfile.tolerate.corruptions", false, ""), + HIVE_RCFILE_RECORD_BUFFER_SIZE("hive.io.rcfile.record.buffer.size", 4194304, ""), // 4M + + PARQUET_MEMORY_POOL_RATIO("parquet.memory.pool.ratio", 0.5f, + "Maximum fraction of heap that can be used by Parquet file writers in one task.\n" + + "It is for avoiding OutOfMemory error in tasks. Work with Parquet 1.6.0 and above.\n" + + "This config parameter is defined in Parquet, so that it does not start with 'hive.'."), + HIVE_PARQUET_TIMESTAMP_SKIP_CONVERSION("hive.parquet.timestamp.skip.conversion", true, + "Current Hive implementation of parquet stores timestamps to UTC, this flag allows skipping of the conversion" + + "on reading parquet files from other tools"), + HIVE_INT_TIMESTAMP_CONVERSION_IN_SECONDS("hive.int.timestamp.conversion.in.seconds", false, + "Boolean/tinyint/smallint/int/bigint value is interpreted as milliseconds during the timestamp conversion.\n" + + "Set this flag to true to interpret the value as seconds to be consistent with float/double." ), + HIVE_ORC_FILE_MEMORY_POOL("hive.exec.orc.memory.pool", 0.5f, + "Maximum fraction of heap that can be used by ORC file writers"), + HIVE_ORC_WRITE_FORMAT("hive.exec.orc.write.format", null, + "Define the version of the file to write. Possible values are 0.11 and 0.12.\n" + + "If this parameter is not defined, ORC will use the run length encoding (RLE)\n" + + "introduced in Hive 0.12. Any value other than 0.11 results in the 0.12 encoding."), + HIVE_ORC_DEFAULT_STRIPE_SIZE("hive.exec.orc.default.stripe.size", + 64L * 1024 * 1024, + "Define the default ORC stripe size, in bytes."), + HIVE_ORC_DEFAULT_BLOCK_SIZE("hive.exec.orc.default.block.size", 256L * 1024 * 1024, + "Define the default file system block size for ORC files."), + + HIVE_ORC_DICTIONARY_KEY_SIZE_THRESHOLD("hive.exec.orc.dictionary.key.size.threshold", 0.8f, + "If the number of keys in a dictionary is greater than this fraction of the total number of\n" + + "non-null rows, turn off dictionary encoding. Use 1 to always use dictionary encoding."), + HIVE_ORC_DEFAULT_ROW_INDEX_STRIDE("hive.exec.orc.default.row.index.stride", 10000, + "Define the default ORC index stride in number of rows. (Stride is the number of rows\n" + + "an index entry represents.)"), + HIVE_ORC_ROW_INDEX_STRIDE_DICTIONARY_CHECK("hive.orc.row.index.stride.dictionary.check", true, + "If enabled dictionary check will happen after first row index stride (default 10000 rows)\n" + + "else dictionary check will happen before writing first stripe. In both cases, the decision\n" + + "to use dictionary or not will be retained thereafter."), + HIVE_ORC_DEFAULT_BUFFER_SIZE("hive.exec.orc.default.buffer.size", 256 * 1024, + "Define the default ORC buffer size, in bytes."), + HIVE_ORC_DEFAULT_BLOCK_PADDING("hive.exec.orc.default.block.padding", true, + "Define the default block padding, which pads stripes to the HDFS block boundaries."), + HIVE_ORC_BLOCK_PADDING_TOLERANCE("hive.exec.orc.block.padding.tolerance", 0.05f, + "Define the tolerance for block padding as a decimal fraction of stripe size (for\n" + + "example, the default value 0.05 is 5% of the stripe size). For the defaults of 64Mb\n" + + "ORC stripe and 256Mb HDFS blocks, the default block padding tolerance of 5% will\n" + + "reserve a maximum of 3.2Mb for padding within the 256Mb block. In that case, if the\n" + + "available size within the block is more than 3.2Mb, a new smaller stripe will be\n" + + "inserted to fit within that space. This will make sure that no stripe written will\n" + + "cross block boundaries and cause remote reads within a node local task."), + HIVE_ORC_DEFAULT_COMPRESS("hive.exec.orc.default.compress", "ZLIB", "Define the default compression codec for ORC file"), + + HIVE_ORC_ENCODING_STRATEGY("hive.exec.orc.encoding.strategy", "SPEED", new StringSet("SPEED", "COMPRESSION"), + "Define the encoding strategy to use while writing data. Changing this will\n" + + "only affect the light weight encoding for integers. This flag will not\n" + + "change the compression level of higher level compression codec (like ZLIB)."), + + HIVE_ORC_COMPRESSION_STRATEGY("hive.exec.orc.compression.strategy", "SPEED", new StringSet("SPEED", "COMPRESSION"), + "Define the compression strategy to use while writing data. \n" + + "This changes the compression level of higher level compression codec (like ZLIB)."), + + HIVE_ORC_SPLIT_STRATEGY("hive.exec.orc.split.strategy", "HYBRID", new StringSet("HYBRID", "BI", "ETL"), + "This is not a user level config. BI strategy is used when the requirement is to spend less time in split generation" + + " as opposed to query execution (split generation does not read or cache file footers)." + + " ETL strategy is used when spending little more time in split generation is acceptable" + + " (split generation reads and caches file footers). HYBRID chooses between the above strategies" + + " based on heuristics."), + + HIVE_ORC_MS_FOOTER_CACHE_ENABLED("hive.orc.splits.ms.footer.cache.enabled", false, + "Whether to enable using file metadata cache in metastore for ORC file footers."), + + HIVE_ORC_INCLUDE_FILE_FOOTER_IN_SPLITS("hive.orc.splits.include.file.footer", false, + "If turned on splits generated by orc will include metadata about the stripes in the file. This\n" + + "data is read remotely (from the client or HS2 machine) and sent to all the tasks."), + HIVE_ORC_SPLIT_DIRECTORY_BATCH_MS("hive.orc.splits.directory.batch.ms", 0, + "How long, in ms, to wait to batch input directories for processing during ORC split\n" + + "generation. 0 means process directories individually. This can increase the number of\n" + + "metastore calls if metastore metadata cache is used."), + HIVE_ORC_INCLUDE_FILE_ID_IN_SPLITS("hive.orc.splits.include.fileid", true, + "Include file ID in splits on file systems thaty support it."), + HIVE_ORC_CACHE_STRIPE_DETAILS_SIZE("hive.orc.cache.stripe.details.size", 10000, + "Max cache size for keeping meta info about orc splits cached in the client."), + HIVE_ORC_COMPUTE_SPLITS_NUM_THREADS("hive.orc.compute.splits.num.threads", 10, + "How many threads orc should use to create splits in parallel."), + HIVE_ORC_SKIP_CORRUPT_DATA("hive.exec.orc.skip.corrupt.data", false, + "If ORC reader encounters corrupt data, this value will be used to determine\n" + + "whether to skip the corrupt data or throw exception. The default behavior is to throw exception."), + + HIVE_ORC_ZEROCOPY("hive.exec.orc.zerocopy", false, + "Use zerocopy reads with ORC. (This requires Hadoop 2.3 or later.)"), + + HIVE_LAZYSIMPLE_EXTENDED_BOOLEAN_LITERAL("hive.lazysimple.extended_boolean_literal", false, + "LazySimpleSerde uses this property to determine if it treats 'T', 't', 'F', 'f',\n" + + "'1', and '0' as extened, legal boolean literal, in addition to 'TRUE' and 'FALSE'.\n" + + "The default is false, which means only 'TRUE' and 'FALSE' are treated as legal\n" + + "boolean literal."), + + HIVESKEWJOIN("hive.optimize.skewjoin", false, + "Whether to enable skew join optimization. \n" + + "The algorithm is as follows: At runtime, detect the keys with a large skew. Instead of\n" + + "processing those keys, store them temporarily in an HDFS directory. In a follow-up map-reduce\n" + + "job, process those skewed keys. The same key need not be skewed for all the tables, and so,\n" + + "the follow-up map-reduce job (for the skewed keys) would be much faster, since it would be a\n" + + "map-join."), + HIVEDYNAMICPARTITIONHASHJOIN("hive.optimize.dynamic.partition.hashjoin", false, + "Whether to enable dynamically partitioned hash join optimization. \n" + + "This setting is also dependent on enabling hive.auto.convert.join"), + HIVECONVERTJOIN("hive.auto.convert.join", true, + "Whether Hive enables the optimization about converting common join into mapjoin based on the input file size"), + HIVECONVERTJOINNOCONDITIONALTASK("hive.auto.convert.join.noconditionaltask", true, + "Whether Hive enables the optimization about converting common join into mapjoin based on the input file size. \n" + + "If this parameter is on, and the sum of size for n-1 of the tables/partitions for a n-way join is smaller than the\n" + + "specified size, the join is directly converted to a mapjoin (there is no conditional task)."), + + HIVECONVERTJOINNOCONDITIONALTASKTHRESHOLD("hive.auto.convert.join.noconditionaltask.size", + 10000000L, + "If hive.auto.convert.join.noconditionaltask is off, this parameter does not take affect. \n" + + "However, if it is on, and the sum of size for n-1 of the tables/partitions for a n-way join is smaller than this size, \n" + + "the join is directly converted to a mapjoin(there is no conditional task). The default is 10MB"), + HIVECONVERTJOINUSENONSTAGED("hive.auto.convert.join.use.nonstaged", false, + "For conditional joins, if input stream from a small alias can be directly applied to join operator without \n" + + "filtering or projection, the alias need not to be pre-staged in distributed cache via mapred local task.\n" + + "Currently, this is not working with vectorization or tez execution engine."), + HIVESKEWJOINKEY("hive.skewjoin.key", 100000, + "Determine if we get a skew key in join. If we see more than the specified number of rows with the same key in join operator,\n" + + "we think the key as a skew join key. "), + HIVESKEWJOINMAPJOINNUMMAPTASK("hive.skewjoin.mapjoin.map.tasks", 10000, + "Determine the number of map task used in the follow up map join job for a skew join.\n" + + "It should be used together with hive.skewjoin.mapjoin.min.split to perform a fine grained control."), + HIVESKEWJOINMAPJOINMINSPLIT("hive.skewjoin.mapjoin.min.split", 33554432L, + "Determine the number of map task at most used in the follow up map join job for a skew join by specifying \n" + + "the minimum split size. It should be used together with hive.skewjoin.mapjoin.map.tasks to perform a fine grained control."), + + HIVESENDHEARTBEAT("hive.heartbeat.interval", 1000, + "Send a heartbeat after this interval - used by mapjoin and filter operators"), + HIVELIMITMAXROWSIZE("hive.limit.row.max.size", 100000L, + "When trying a smaller subset of data for simple LIMIT, how much size we need to guarantee each row to have at least."), + HIVELIMITOPTLIMITFILE("hive.limit.optimize.limit.file", 10, + "When trying a smaller subset of data for simple LIMIT, maximum number of files we can sample."), + HIVELIMITOPTENABLE("hive.limit.optimize.enable", false, + "Whether to enable to optimization to trying a smaller subset of data for simple LIMIT first."), + HIVELIMITOPTMAXFETCH("hive.limit.optimize.fetch.max", 50000, + "Maximum number of rows allowed for a smaller subset of data for simple LIMIT, if it is a fetch query. \n" + + "Insert queries are not restricted by this limit."), + HIVELIMITPUSHDOWNMEMORYUSAGE("hive.limit.pushdown.memory.usage", 0.1f, new RatioValidator(), + "The fraction of available memory to be used for buffering rows in Reducesink operator for limit pushdown optimization."), + HIVELIMITTABLESCANPARTITION("hive.limit.query.max.table.partition", -1, + "This controls how many partitions can be scanned for each partitioned table.\n" + + "The default value \"-1\" means no limit."), + + HIVEHASHTABLEKEYCOUNTADJUSTMENT("hive.hashtable.key.count.adjustment", 1.0f, + "Adjustment to mapjoin hashtable size derived from table and column statistics; the estimate" + + " of the number of keys is divided by this value. If the value is 0, statistics are not used" + + "and hive.hashtable.initialCapacity is used instead."), + HIVEHASHTABLETHRESHOLD("hive.hashtable.initialCapacity", 100000, "Initial capacity of " + + "mapjoin hashtable if statistics are absent, or if hive.hashtable.stats.key.estimate.adjustment is set to 0"), + HIVEHASHTABLELOADFACTOR("hive.hashtable.loadfactor", (float) 0.75, ""), + HIVEHASHTABLEFOLLOWBYGBYMAXMEMORYUSAGE("hive.mapjoin.followby.gby.localtask.max.memory.usage", (float) 0.55, + "This number means how much memory the local task can take to hold the key/value into an in-memory hash table \n" + + "when this map join is followed by a group by. If the local task's memory usage is more than this number, \n" + + "the local task will abort by itself. It means the data of the small table is too large to be held in memory."), + HIVEHASHTABLEMAXMEMORYUSAGE("hive.mapjoin.localtask.max.memory.usage", (float) 0.90, + "This number means how much memory the local task can take to hold the key/value into an in-memory hash table. \n" + + "If the local task's memory usage is more than this number, the local task will abort by itself. \n" + + "It means the data of the small table is too large to be held in memory."), + HIVEHASHTABLESCALE("hive.mapjoin.check.memory.rows", (long)100000, + "The number means after how many rows processed it needs to check the memory usage"), + + HIVEDEBUGLOCALTASK("hive.debug.localtask",false, ""), + + HIVEINPUTFORMAT("hive.input.format", "org.apache.hadoop.hive.ql.io.CombineHiveInputFormat", + "The default input format. Set this to HiveInputFormat if you encounter problems with CombineHiveInputFormat."), + HIVETEZINPUTFORMAT("hive.tez.input.format", "org.apache.hadoop.hive.ql.io.HiveInputFormat", + "The default input format for tez. Tez groups splits in the AM."), + + HIVETEZCONTAINERSIZE("hive.tez.container.size", -1, + "By default Tez will spawn containers of the size of a mapper. This can be used to overwrite."), + HIVETEZCPUVCORES("hive.tez.cpu.vcores", -1, + "By default Tez will ask for however many cpus map-reduce is configured to use per container.\n" + + "This can be used to overwrite."), + HIVETEZJAVAOPTS("hive.tez.java.opts", null, + "By default Tez will use the Java options from map tasks. This can be used to overwrite."), + HIVETEZLOGLEVEL("hive.tez.log.level", "INFO", + "The log level to use for tasks executing as part of the DAG.\n" + + "Used only if hive.tez.java.opts is used to configure Java options."), + + HIVEOPTIMIZEBUCKETINGSORTING("hive.optimize.bucketingsorting", true, + "Don't create a reducer for enforcing \n" + + "bucketing/sorting for queries of the form: \n" + + "insert overwrite table T2 select * from T1;\n" + + "where T1 and T2 are bucketed/sorted by the same keys into the same number of buckets."), + HIVEPARTITIONER("hive.mapred.partitioner", "org.apache.hadoop.hive.ql.io.DefaultHivePartitioner", ""), + HIVEENFORCESORTMERGEBUCKETMAPJOIN("hive.enforce.sortmergebucketmapjoin", false, + "If the user asked for sort-merge bucketed map-side join, and it cannot be performed, should the query fail or not ?"), + HIVEENFORCEBUCKETMAPJOIN("hive.enforce.bucketmapjoin", false, + "If the user asked for bucketed map-side join, and it cannot be performed, \n" + + "should the query fail or not ? For example, if the buckets in the tables being joined are\n" + + "not a multiple of each other, bucketed map-side join cannot be performed, and the\n" + + "query will fail if hive.enforce.bucketmapjoin is set to true."), + + HIVE_AUTO_SORTMERGE_JOIN("hive.auto.convert.sortmerge.join", false, + "Will the join be automatically converted to a sort-merge join, if the joined tables pass the criteria for sort-merge join."), + HIVE_AUTO_SORTMERGE_JOIN_BIGTABLE_SELECTOR( + "hive.auto.convert.sortmerge.join.bigtable.selection.policy", + "org.apache.hadoop.hive.ql.optimizer.AvgPartitionSizeBasedBigTableSelectorForAutoSMJ", + "The policy to choose the big table for automatic conversion to sort-merge join. \n" + + "By default, the table with the largest partitions is assigned the big table. All policies are:\n" + + ". based on position of the table - the leftmost table is selected\n" + + "org.apache.hadoop.hive.ql.optimizer.LeftmostBigTableSMJ.\n" + + ". based on total size (all the partitions selected in the query) of the table \n" + + "org.apache.hadoop.hive.ql.optimizer.TableSizeBasedBigTableSelectorForAutoSMJ.\n" + + ". based on average size (all the partitions selected in the query) of the table \n" + + "org.apache.hadoop.hive.ql.optimizer.AvgPartitionSizeBasedBigTableSelectorForAutoSMJ.\n" + + "New policies can be added in future."), + HIVE_AUTO_SORTMERGE_JOIN_TOMAPJOIN( + "hive.auto.convert.sortmerge.join.to.mapjoin", false, + "If hive.auto.convert.sortmerge.join is set to true, and a join was converted to a sort-merge join, \n" + + "this parameter decides whether each table should be tried as a big table, and effectively a map-join should be\n" + + "tried. That would create a conditional task with n+1 children for a n-way join (1 child for each table as the\n" + + "big table), and the backup task will be the sort-merge join. In some cases, a map-join would be faster than a\n" + + "sort-merge join, if there is no advantage of having the output bucketed and sorted. For example, if a very big sorted\n" + + "and bucketed table with few files (say 10 files) are being joined with a very small sorter and bucketed table\n" + + "with few files (10 files), the sort-merge join will only use 10 mappers, and a simple map-only join might be faster\n" + + "if the complete small table can fit in memory, and a map-join can be performed."), + + HIVESCRIPTOPERATORTRUST("hive.exec.script.trust", false, ""), + HIVEROWOFFSET("hive.exec.rowoffset", false, + "Whether to provide the row offset virtual column"), + + // Optimizer + HIVEOPTINDEXFILTER("hive.optimize.index.filter", false, + "Whether to enable automatic use of indexes"), + HIVEINDEXAUTOUPDATE("hive.optimize.index.autoupdate", false, + "Whether to update stale indexes automatically"), + HIVEOPTPPD("hive.optimize.ppd", true, + "Whether to enable predicate pushdown"), + HIVEPPDRECOGNIZETRANSITIVITY("hive.ppd.recognizetransivity", true, + "Whether to transitively replicate predicate filters over equijoin conditions."), + HIVEPPDREMOVEDUPLICATEFILTERS("hive.ppd.remove.duplicatefilters", true, + "Whether to push predicates down into storage handlers. Ignored when hive.optimize.ppd is false."), + HIVEPOINTLOOKUPOPTIMIZER("hive.optimize.point.lookup", true, + "Whether to transform OR clauses in Filter operators into IN clauses"), + HIVEPOINTLOOKUPOPTIMIZERMIN("hive.optimize.point.lookup.min", 31, + "Minimum number of OR clauses needed to transform into IN clauses"), + HIVEPARTITIONCOLUMNSEPARATOR("hive.optimize.partition.columns.separate", true, + "Extract partition columns from IN clauses"), + // Constant propagation optimizer + HIVEOPTCONSTANTPROPAGATION("hive.optimize.constant.propagation", true, "Whether to enable constant propagation optimizer"), + HIVEIDENTITYPROJECTREMOVER("hive.optimize.remove.identity.project", true, "Removes identity project from operator tree"), + HIVEMETADATAONLYQUERIES("hive.optimize.metadataonly", true, ""), + HIVENULLSCANOPTIMIZE("hive.optimize.null.scan", true, "Dont scan relations which are guaranteed to not generate any rows"), + HIVEOPTPPD_STORAGE("hive.optimize.ppd.storage", true, + "Whether to push predicates down to storage handlers"), + HIVEOPTGROUPBY("hive.optimize.groupby", true, + "Whether to enable the bucketed group by from bucketed partitions/tables."), + HIVEOPTBUCKETMAPJOIN("hive.optimize.bucketmapjoin", false, + "Whether to try bucket mapjoin"), + HIVEOPTSORTMERGEBUCKETMAPJOIN("hive.optimize.bucketmapjoin.sortedmerge", false, + "Whether to try sorted bucket merge map join"), + HIVEOPTREDUCEDEDUPLICATION("hive.optimize.reducededuplication", true, + "Remove extra map-reduce jobs if the data is already clustered by the same key which needs to be used again. \n" + + "This should always be set to true. Since it is a new feature, it has been made configurable."), + HIVEOPTREDUCEDEDUPLICATIONMINREDUCER("hive.optimize.reducededuplication.min.reducer", 4, + "Reduce deduplication merges two RSs by moving key/parts/reducer-num of the child RS to parent RS. \n" + + "That means if reducer-num of the child RS is fixed (order by or forced bucketing) and small, it can make very slow, single MR.\n" + + "The optimization will be automatically disabled if number of reducers would be less than specified value."), + + HIVEOPTSORTDYNAMICPARTITION("hive.optimize.sort.dynamic.partition", false, + "When enabled dynamic partitioning column will be globally sorted.\n" + + "This way we can keep only one record writer open for each partition value\n" + + "in the reducer thereby reducing the memory pressure on reducers."), + + HIVESAMPLINGFORORDERBY("hive.optimize.sampling.orderby", false, "Uses sampling on order-by clause for parallel execution."), + HIVESAMPLINGNUMBERFORORDERBY("hive.optimize.sampling.orderby.number", 1000, "Total number of samples to be obtained."), + HIVESAMPLINGPERCENTFORORDERBY("hive.optimize.sampling.orderby.percent", 0.1f, new RatioValidator(), + "Probability with which a row will be chosen."), + HIVEOPTIMIZEDISTINCTREWRITE("hive.optimize.distinct.rewrite", true, "When applicable this " + + "optimization rewrites distinct aggregates from a single stage to multi-stage " + + "aggregation. This may not be optimal in all cases. Ideally, whether to trigger it or " + + "not should be cost based decision. Until Hive formalizes cost model for this, this is config driven."), + // whether to optimize union followed by select followed by filesink + // It creates sub-directories in the final output, so should not be turned on in systems + // where MAPREDUCE-1501 is not present + HIVE_OPTIMIZE_UNION_REMOVE("hive.optimize.union.remove", false, + "Whether to remove the union and push the operators between union and the filesink above union. \n" + + "This avoids an extra scan of the output by union. This is independently useful for union\n" + + "queries, and specially useful when hive.optimize.skewjoin.compiletime is set to true, since an\n" + + "extra union is inserted.\n" + + "\n" + + "The merge is triggered if either of hive.merge.mapfiles or hive.merge.mapredfiles is set to true.\n" + + "If the user has set hive.merge.mapfiles to true and hive.merge.mapredfiles to false, the idea was the\n" + + "number of reducers are few, so the number of files anyway are small. However, with this optimization,\n" + + "we are increasing the number of files possibly by a big margin. So, we merge aggressively."), + HIVEOPTCORRELATION("hive.optimize.correlation", false, "exploit intra-query correlations."), + + HIVE_OPTIMIZE_LIMIT_JOIN_TRANSPOSE("hive.optimize.limitjointranspose", false, + "Whether to push a limit through left/right outer join. If the value is true and the size of the outer\n" + + "input is reduced enough (as specified in hive.optimize.limitjointranspose.reduction), the limit is pushed\n" + + "to the outer input; to remain semantically correct, the limit is kept on top of the join too."), + HIVE_OPTIMIZE_LIMIT_JOIN_TRANSPOSE_REDUCTION_PERCENTAGE("hive.optimize.limitjointranspose.reductionpercentage", 1.0f, + "When hive.optimize.limitjointranspose is true, this variable specifies the minimal reduction of the\n" + + "size of the outer input of the join that we should get in order to apply the rule."), + HIVE_OPTIMIZE_LIMIT_JOIN_TRANSPOSE_REDUCTION_TUPLES("hive.optimize.limitjointranspose.reductiontuples", (long) 0, + "When hive.optimize.limitjointranspose is true, this variable specifies the minimal reduction in the\n" + + "number of tuples of the outer input of the join that you should get in order to apply the rule."), + + HIVE_OPTIMIZE_SKEWJOIN_COMPILETIME("hive.optimize.skewjoin.compiletime", false, + "Whether to create a separate plan for skewed keys for the tables in the join.\n" + + "This is based on the skewed keys stored in the metadata. At compile time, the plan is broken\n" + + "into different joins: one for the skewed keys, and the other for the remaining keys. And then,\n" + + "a union is performed for the 2 joins generated above. So unless the same skewed key is present\n" + + "in both the joined tables, the join for the skewed key will be performed as a map-side join.\n" + + "\n" + + "The main difference between this parameter and hive.optimize.skewjoin is that this parameter\n" + + "uses the skew information stored in the metastore to optimize the plan at compile time itself.\n" + + "If there is no skew information in the metadata, this parameter will not have any affect.\n" + + "Both hive.optimize.skewjoin.compiletime and hive.optimize.skewjoin should be set to true.\n" + + "Ideally, hive.optimize.skewjoin should be renamed as hive.optimize.skewjoin.runtime, but not doing\n" + + "so for backward compatibility.\n" + + "\n" + + "If the skew information is correctly stored in the metadata, hive.optimize.skewjoin.compiletime\n" + + "would change the query plan to take care of it, and hive.optimize.skewjoin will be a no-op."), + + // Indexes + HIVEOPTINDEXFILTER_COMPACT_MINSIZE("hive.optimize.index.filter.compact.minsize", (long) 5 * 1024 * 1024 * 1024, + "Minimum size (in bytes) of the inputs on which a compact index is automatically used."), // 5G + HIVEOPTINDEXFILTER_COMPACT_MAXSIZE("hive.optimize.index.filter.compact.maxsize", (long) -1, + "Maximum size (in bytes) of the inputs on which a compact index is automatically used. A negative number is equivalent to infinity."), // infinity + HIVE_INDEX_COMPACT_QUERY_MAX_ENTRIES("hive.index.compact.query.max.entries", (long) 10000000, + "The maximum number of index entries to read during a query that uses the compact index. Negative value is equivalent to infinity."), // 10M + HIVE_INDEX_COMPACT_QUERY_MAX_SIZE("hive.index.compact.query.max.size", (long) 10 * 1024 * 1024 * 1024, + "The maximum number of bytes that a query using the compact index can read. Negative value is equivalent to infinity."), // 10G + HIVE_INDEX_COMPACT_BINARY_SEARCH("hive.index.compact.binary.search", true, + "Whether or not to use a binary search to find the entries in an index table that match the filter, where possible"), + + // Statistics + HIVESTATSAUTOGATHER("hive.stats.autogather", true, + "A flag to gather statistics automatically during the INSERT OVERWRITE command."), + HIVESTATSDBCLASS("hive.stats.dbclass", "fs", new PatternSet("custom", "fs"), + "The storage that stores temporary Hive statistics. In filesystem based statistics collection ('fs'), \n" + + "each task writes statistics it has collected in a file on the filesystem, which will be aggregated \n" + + "after the job has finished. Supported values are fs (filesystem) and custom as defined in StatsSetupConst.java."), // StatsSetupConst.StatDB + HIVE_STATS_DEFAULT_PUBLISHER("hive.stats.default.publisher", "", + "The Java class (implementing the StatsPublisher interface) that is used by default if hive.stats.dbclass is custom type."), + HIVE_STATS_DEFAULT_AGGREGATOR("hive.stats.default.aggregator", "", + "The Java class (implementing the StatsAggregator interface) that is used by default if hive.stats.dbclass is custom type."), + HIVE_STATS_ATOMIC("hive.stats.atomic", false, + "whether to update metastore stats only if all stats are available"), + HIVE_STATS_COLLECT_RAWDATASIZE("hive.stats.collect.rawdatasize", true, + "should the raw data size be collected when analyzing tables"), + CLIENT_STATS_COUNTERS("hive.client.stats.counters", "", + "Subset of counters that should be of interest for hive.client.stats.publishers (when one wants to limit their publishing). \n" + + "Non-display names should be used"), + //Subset of counters that should be of interest for hive.client.stats.publishers (when one wants to limit their publishing). Non-display names should be used". + HIVE_STATS_RELIABLE("hive.stats.reliable", false, + "Whether queries will fail because stats cannot be collected completely accurately. \n" + + "If this is set to true, reading/writing from/into a partition may fail because the stats\n" + + "could not be computed accurately."), + HIVE_STATS_COLLECT_PART_LEVEL_STATS("hive.analyze.stmt.collect.partlevel.stats", true, + "analyze table T compute statistics for columns. Queries like these should compute partition" + + "level stats for partitioned table even when no part spec is specified."), + HIVE_STATS_GATHER_NUM_THREADS("hive.stats.gather.num.threads", 10, + "Number of threads used by partialscan/noscan analyze command for partitioned tables.\n" + + "This is applicable only for file formats that implement StatsProvidingRecordReader (like ORC)."), + // Collect table access keys information for operators that can benefit from bucketing + HIVE_STATS_COLLECT_TABLEKEYS("hive.stats.collect.tablekeys", false, + "Whether join and group by keys on tables are derived and maintained in the QueryPlan.\n" + + "This is useful to identify how tables are accessed and to determine if they should be bucketed."), + // Collect column access information + HIVE_STATS_COLLECT_SCANCOLS("hive.stats.collect.scancols", false, + "Whether column accesses are tracked in the QueryPlan.\n" + + "This is useful to identify how tables are accessed and to determine if there are wasted columns that can be trimmed."), + // standard error allowed for ndv estimates. A lower value indicates higher accuracy and a + // higher compute cost. + HIVE_STATS_NDV_ERROR("hive.stats.ndv.error", (float)20.0, + "Standard error expressed in percentage. Provides a tradeoff between accuracy and compute cost. \n" + + "A lower value for error indicates higher accuracy and a higher compute cost."), + HIVE_METASTORE_STATS_NDV_DENSITY_FUNCTION("hive.metastore.stats.ndv.densityfunction", false, + "Whether to use density function to estimate the NDV for the whole table based on the NDV of partitions"), + HIVE_STATS_KEY_PREFIX("hive.stats.key.prefix", "", "", true), // internal usage only + // if length of variable length data type cannot be determined this length will be used. + HIVE_STATS_MAX_VARIABLE_LENGTH("hive.stats.max.variable.length", 100, + "To estimate the size of data flowing through operators in Hive/Tez(for reducer estimation etc.),\n" + + "average row size is multiplied with the total number of rows coming out of each operator.\n" + + "Average row size is computed from average column size of all columns in the row. In the absence\n" + + "of column statistics, for variable length columns (like string, bytes etc.), this value will be\n" + + "used. For fixed length columns their corresponding Java equivalent sizes are used\n" + + "(float - 4 bytes, double - 8 bytes etc.)."), + // if number of elements in list cannot be determined, this value will be used + HIVE_STATS_LIST_NUM_ENTRIES("hive.stats.list.num.entries", 10, + "To estimate the size of data flowing through operators in Hive/Tez(for reducer estimation etc.),\n" + + "average row size is multiplied with the total number of rows coming out of each operator.\n" + + "Average row size is computed from average column size of all columns in the row. In the absence\n" + + "of column statistics and for variable length complex columns like list, the average number of\n" + + "entries/values can be specified using this config."), + // if number of elements in map cannot be determined, this value will be used + HIVE_STATS_MAP_NUM_ENTRIES("hive.stats.map.num.entries", 10, + "To estimate the size of data flowing through operators in Hive/Tez(for reducer estimation etc.),\n" + + "average row size is multiplied with the total number of rows coming out of each operator.\n" + + "Average row size is computed from average column size of all columns in the row. In the absence\n" + + "of column statistics and for variable length complex columns like map, the average number of\n" + + "entries/values can be specified using this config."), + // statistics annotation fetches stats for each partition, which can be expensive. turning + // this off will result in basic sizes being fetched from namenode instead + HIVE_STATS_FETCH_PARTITION_STATS("hive.stats.fetch.partition.stats", true, + "Annotation of operator tree with statistics information requires partition level basic\n" + + "statistics like number of rows, data size and file size. Partition statistics are fetched from\n" + + "metastore. Fetching partition statistics for each needed partition can be expensive when the\n" + + "number of partitions is high. This flag can be used to disable fetching of partition statistics\n" + + "from metastore. When this flag is disabled, Hive will make calls to filesystem to get file sizes\n" + + "and will estimate the number of rows from row schema."), + // statistics annotation fetches column statistics for all required columns which can + // be very expensive sometimes + HIVE_STATS_FETCH_COLUMN_STATS("hive.stats.fetch.column.stats", false, + "Annotation of operator tree with statistics information requires column statistics.\n" + + "Column statistics are fetched from metastore. Fetching column statistics for each needed column\n" + + "can be expensive when the number of columns is high. This flag can be used to disable fetching\n" + + "of column statistics from metastore."), + // in the absence of column statistics, the estimated number of rows/data size that will + // be emitted from join operator will depend on this factor + HIVE_STATS_JOIN_FACTOR("hive.stats.join.factor", (float) 1.1, + "Hive/Tez optimizer estimates the data size flowing through each of the operators. JOIN operator\n" + + "uses column statistics to estimate the number of rows flowing out of it and hence the data size.\n" + + "In the absence of column statistics, this factor determines the amount of rows that flows out\n" + + "of JOIN operator."), + // in the absence of uncompressed/raw data size, total file size will be used for statistics + // annotation. But the file may be compressed, encoded and serialized which may be lesser in size + // than the actual uncompressed/raw data size. This factor will be multiplied to file size to estimate + // the raw data size. + HIVE_STATS_DESERIALIZATION_FACTOR("hive.stats.deserialization.factor", (float) 1.0, + "Hive/Tez optimizer estimates the data size flowing through each of the operators. In the absence\n" + + "of basic statistics like number of rows and data size, file size is used to estimate the number\n" + + "of rows and data size. Since files in tables/partitions are serialized (and optionally\n" + + "compressed) the estimates of number of rows and data size cannot be reliably determined.\n" + + "This factor is multiplied with the file size to account for serialization and compression."), + + // Concurrency + HIVE_SUPPORT_CONCURRENCY("hive.support.concurrency", false, + "Whether Hive supports concurrency control or not. \n" + + "A ZooKeeper instance must be up and running when using zookeeper Hive lock manager "), + HIVE_LOCK_MANAGER("hive.lock.manager", "org.apache.hadoop.hive.ql.lockmgr.zookeeper.ZooKeeperHiveLockManager", ""), + HIVE_LOCK_NUMRETRIES("hive.lock.numretries", 100, + "The number of times you want to try to get all the locks"), + HIVE_UNLOCK_NUMRETRIES("hive.unlock.numretries", 10, + "The number of times you want to retry to do one unlock"), + HIVE_LOCK_SLEEP_BETWEEN_RETRIES("hive.lock.sleep.between.retries", "60s", + new TimeValidator(TimeUnit.SECONDS), + "The sleep time between various retries"), + HIVE_LOCK_MAPRED_ONLY("hive.lock.mapred.only.operation", false, + "This param is to control whether or not only do lock on queries\n" + + "that need to execute at least one mapred job."), + + // Zookeeper related configs + HIVE_ZOOKEEPER_QUORUM("hive.zookeeper.quorum", "", + "List of ZooKeeper servers to talk to. This is needed for: \n" + + "1. Read/write locks - when hive.lock.manager is set to \n" + + "org.apache.hadoop.hive.ql.lockmgr.zookeeper.ZooKeeperHiveLockManager, \n" + + "2. When HiveServer2 supports service discovery via Zookeeper.\n" + + "3. For delegation token storage if zookeeper store is used, if\n" + + "hive.cluster.delegation.token.store.zookeeper.connectString is not set"), + + HIVE_ZOOKEEPER_CLIENT_PORT("hive.zookeeper.client.port", "2181", + "The port of ZooKeeper servers to talk to.\n" + + "If the list of Zookeeper servers specified in hive.zookeeper.quorum\n" + + "does not contain port numbers, this value is used."), + HIVE_ZOOKEEPER_SESSION_TIMEOUT("hive.zookeeper.session.timeout", "1200000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "ZooKeeper client's session timeout (in milliseconds). The client is disconnected, and as a result, all locks released, \n" + + "if a heartbeat is not sent in the timeout."), + HIVE_ZOOKEEPER_NAMESPACE("hive.zookeeper.namespace", "hive_zookeeper_namespace", + "The parent node under which all ZooKeeper nodes are created."), + HIVE_ZOOKEEPER_CLEAN_EXTRA_NODES("hive.zookeeper.clean.extra.nodes", false, + "Clean extra nodes at the end of the session."), + HIVE_ZOOKEEPER_CONNECTION_MAX_RETRIES("hive.zookeeper.connection.max.retries", 3, + "Max number of times to retry when connecting to the ZooKeeper server."), + HIVE_ZOOKEEPER_CONNECTION_BASESLEEPTIME("hive.zookeeper.connection.basesleeptime", "1000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Initial amount of time (in milliseconds) to wait between retries\n" + + "when connecting to the ZooKeeper server when using ExponentialBackoffRetry policy."), + + // Transactions + HIVE_TXN_MANAGER("hive.txn.manager", + "org.apache.hadoop.hive.ql.lockmgr.DummyTxnManager", + "Set to org.apache.hadoop.hive.ql.lockmgr.DbTxnManager as part of turning on Hive\n" + + "transactions, which also requires appropriate settings for hive.compactor.initiator.on,\n" + + "hive.compactor.worker.threads, hive.support.concurrency (true), hive.enforce.bucketing\n" + + "(true), and hive.exec.dynamic.partition.mode (nonstrict).\n" + + "The default DummyTxnManager replicates pre-Hive-0.13 behavior and provides\n" + + "no transactions."), + HIVE_TXN_TIMEOUT("hive.txn.timeout", "300s", new TimeValidator(TimeUnit.SECONDS), + "time after which transactions are declared aborted if the client has not sent a heartbeat."), + + HIVE_TXN_MAX_OPEN_BATCH("hive.txn.max.open.batch", 1000, + "Maximum number of transactions that can be fetched in one call to open_txns().\n" + + "This controls how many transactions streaming agents such as Flume or Storm open\n" + + "simultaneously. The streaming agent then writes that number of entries into a single\n" + + "file (per Flume agent or Storm bolt). Thus increasing this value decreases the number\n" + + "of delta files created by streaming agents. But it also increases the number of open\n" + + "transactions that Hive has to track at any given time, which may negatively affect\n" + + "read performance."), + + HIVE_COMPACTOR_INITIATOR_ON("hive.compactor.initiator.on", false, + "Whether to run the initiator and cleaner threads on this metastore instance or not.\n" + + "Set this to true on one instance of the Thrift metastore service as part of turning\n" + + "on Hive transactions. For a complete list of parameters required for turning on\n" + + "transactions, see hive.txn.manager."), + + HIVE_COMPACTOR_WORKER_THREADS("hive.compactor.worker.threads", 0, + "How many compactor worker threads to run on this metastore instance. Set this to a\n" + + "positive number on one or more instances of the Thrift metastore service as part of\n" + + "turning on Hive transactions. For a complete list of parameters required for turning\n" + + "on transactions, see hive.txn.manager.\n" + + "Worker threads spawn MapReduce jobs to do compactions. They do not do the compactions\n" + + "themselves. Increasing the number of worker threads will decrease the time it takes\n" + + "tables or partitions to be compacted once they are determined to need compaction.\n" + + "It will also increase the background load on the Hadoop cluster as more MapReduce jobs\n" + + "will be running in the background."), + + HIVE_COMPACTOR_WORKER_TIMEOUT("hive.compactor.worker.timeout", "86400s", + new TimeValidator(TimeUnit.SECONDS), + "Time in seconds after which a compaction job will be declared failed and the\n" + + "compaction re-queued."), + + HIVE_COMPACTOR_CHECK_INTERVAL("hive.compactor.check.interval", "300s", + new TimeValidator(TimeUnit.SECONDS), + "Time in seconds between checks to see if any tables or partitions need to be\n" + + "compacted. This should be kept high because each check for compaction requires\n" + + "many calls against the NameNode.\n" + + "Decreasing this value will reduce the time it takes for compaction to be started\n" + + "for a table or partition that requires compaction. However, checking if compaction\n" + + "is needed requires several calls to the NameNode for each table or partition that\n" + + "has had a transaction done on it since the last major compaction. So decreasing this\n" + + "value will increase the load on the NameNode."), + + HIVE_COMPACTOR_DELTA_NUM_THRESHOLD("hive.compactor.delta.num.threshold", 10, + "Number of delta directories in a table or partition that will trigger a minor\n" + + "compaction."), + + HIVE_COMPACTOR_DELTA_PCT_THRESHOLD("hive.compactor.delta.pct.threshold", 0.1f, + "Percentage (fractional) size of the delta files relative to the base that will trigger\n" + + "a major compaction. (1.0 = 100%, so the default 0.1 = 10%.)"), + COMPACTOR_MAX_NUM_DELTA("hive.compactor.max.num.delta", 500, "Maximum number of delta files that " + + "the compactor will attempt to handle in a single job."), + + HIVE_COMPACTOR_ABORTEDTXN_THRESHOLD("hive.compactor.abortedtxn.threshold", 1000, + "Number of aborted transactions involving a given table or partition that will trigger\n" + + "a major compaction."), + + HIVE_COMPACTOR_CLEANER_RUN_INTERVAL("hive.compactor.cleaner.run.interval", "5000ms", + new TimeValidator(TimeUnit.MILLISECONDS), "Time between runs of the cleaner thread"), + COMPACTOR_JOB_QUEUE("hive.compactor.job.queue", "", "Used to specify name of Hadoop queue to which\n" + + "Compaction jobs will be submitted. Set to empty string to let Hadoop choose the queue."), + HIVE_TIMEDOUT_TXN_REAPER_START("hive.timedout.txn.reaper.start", "100s", + new TimeValidator(TimeUnit.MILLISECONDS), "Time delay of 1st reaper run after metastore start"), + HIVE_TIMEDOUT_TXN_REAPER_INTERVAL("hive.timedout.txn.reaper.interval", "180s", + new TimeValidator(TimeUnit.MILLISECONDS), "Time interval describing how often the reaper runs"), + + // For HBase storage handler + HIVE_HBASE_WAL_ENABLED("hive.hbase.wal.enabled", true, + "Whether writes to HBase should be forced to the write-ahead log. \n" + + "Disabling this improves HBase write performance at the risk of lost writes in case of a crash."), + HIVE_HBASE_GENERATE_HFILES("hive.hbase.generatehfiles", false, + "True when HBaseStorageHandler should generate hfiles instead of operate against the online table."), + HIVE_HBASE_SNAPSHOT_NAME("hive.hbase.snapshot.name", null, "The HBase table snapshot name to use."), + HIVE_HBASE_SNAPSHOT_RESTORE_DIR("hive.hbase.snapshot.restoredir", "/tmp", "The directory in which to " + + "restore the HBase table snapshot."), + + // For har files + HIVEARCHIVEENABLED("hive.archive.enabled", false, "Whether archiving operations are permitted"), + + HIVEOPTGBYUSINGINDEX("hive.optimize.index.groupby", false, + "Whether to enable optimization of group-by queries using Aggregate indexes."), + + HIVEOUTERJOINSUPPORTSFILTERS("hive.outerjoin.supports.filters", true, ""), + + HIVEFETCHTASKCONVERSION("hive.fetch.task.conversion", "more", new StringSet("none", "minimal", "more"), + "Some select queries can be converted to single FETCH task minimizing latency.\n" + + "Currently the query should be single sourced not having any subquery and should not have\n" + + "any aggregations or distincts (which incurs RS), lateral views and joins.\n" + + "0. none : disable hive.fetch.task.conversion\n" + + "1. minimal : SELECT STAR, FILTER on partition columns, LIMIT only\n" + + "2. more : SELECT, FILTER, LIMIT only (support TABLESAMPLE and virtual columns)" + ), + HIVEFETCHTASKCONVERSIONTHRESHOLD("hive.fetch.task.conversion.threshold", 1073741824L, + "Input threshold for applying hive.fetch.task.conversion. If target table is native, input length\n" + + "is calculated by summation of file lengths. If it's not native, storage handler for the table\n" + + "can optionally implement org.apache.hadoop.hive.ql.metadata.InputEstimator interface."), + + HIVEFETCHTASKAGGR("hive.fetch.task.aggr", false, + "Aggregation queries with no group-by clause (for example, select count(*) from src) execute\n" + + "final aggregations in single reduce task. If this is set true, Hive delegates final aggregation\n" + + "stage to fetch task, possibly decreasing the query time."), + + HIVEOPTIMIZEMETADATAQUERIES("hive.compute.query.using.stats", false, + "When set to true Hive will answer a few queries like count(1) purely using stats\n" + + "stored in metastore. For basic stats collection turn on the config hive.stats.autogather to true.\n" + + "For more advanced stats collection need to run analyze table queries."), + + // Serde for FetchTask + HIVEFETCHOUTPUTSERDE("hive.fetch.output.serde", "org.apache.hadoop.hive.serde2.DelimitedJSONSerDe", + "The SerDe used by FetchTask to serialize the fetch output."), + + HIVEEXPREVALUATIONCACHE("hive.cache.expr.evaluation", true, + "If true, the evaluation result of a deterministic expression referenced twice or more\n" + + "will be cached.\n" + + "For example, in a filter condition like '.. where key + 10 = 100 or key + 10 = 0'\n" + + "the expression 'key + 10' will be evaluated/cached once and reused for the following\n" + + "expression ('key + 10 = 0'). Currently, this is applied only to expressions in select\n" + + "or filter operators."), + + // Hive Variables + HIVEVARIABLESUBSTITUTE("hive.variable.substitute", true, + "This enables substitution using syntax like ${var} ${system:var} and ${env:var}."), + HIVEVARIABLESUBSTITUTEDEPTH("hive.variable.substitute.depth", 40, + "The maximum replacements the substitution engine will do."), + + HIVECONFVALIDATION("hive.conf.validation", true, + "Enables type checking for registered Hive configurations"), + + SEMANTIC_ANALYZER_HOOK("hive.semantic.analyzer.hook", "", ""), + HIVE_TEST_AUTHORIZATION_SQLSTD_HS2_MODE( + "hive.test.authz.sstd.hs2.mode", false, "test hs2 mode from .q tests", true), + HIVE_AUTHORIZATION_ENABLED("hive.security.authorization.enabled", false, + "enable or disable the Hive client authorization"), + HIVE_AUTHORIZATION_MANAGER("hive.security.authorization.manager", + "org.apache.hadoop.hive.ql.security.authorization.DefaultHiveAuthorizationProvider", + "The Hive client authorization manager class name. The user defined authorization class should implement \n" + + "interface org.apache.hadoop.hive.ql.security.authorization.HiveAuthorizationProvider."), + HIVE_AUTHENTICATOR_MANAGER("hive.security.authenticator.manager", + "org.apache.hadoop.hive.ql.security.HadoopDefaultAuthenticator", + "hive client authenticator manager class name. The user defined authenticator should implement \n" + + "interface org.apache.hadoop.hive.ql.security.HiveAuthenticationProvider."), + HIVE_METASTORE_AUTHORIZATION_MANAGER("hive.security.metastore.authorization.manager", + "org.apache.hadoop.hive.ql.security.authorization.DefaultHiveMetastoreAuthorizationProvider", + "Names of authorization manager classes (comma separated) to be used in the metastore\n" + + "for authorization. The user defined authorization class should implement interface\n" + + "org.apache.hadoop.hive.ql.security.authorization.HiveMetastoreAuthorizationProvider.\n" + + "All authorization manager classes have to successfully authorize the metastore API\n" + + "call for the command execution to be allowed."), + HIVE_METASTORE_AUTHORIZATION_AUTH_READS("hive.security.metastore.authorization.auth.reads", true, + "If this is true, metastore authorizer authorizes read actions on database, table"), + HIVE_METASTORE_AUTHENTICATOR_MANAGER("hive.security.metastore.authenticator.manager", + "org.apache.hadoop.hive.ql.security.HadoopDefaultMetastoreAuthenticator", + "authenticator manager class name to be used in the metastore for authentication. \n" + + "The user defined authenticator should implement interface org.apache.hadoop.hive.ql.security.HiveAuthenticationProvider."), + HIVE_AUTHORIZATION_TABLE_USER_GRANTS("hive.security.authorization.createtable.user.grants", "", + "the privileges automatically granted to some users whenever a table gets created.\n" + + "An example like \"userX,userY:select;userZ:create\" will grant select privilege to userX and userY,\n" + + "and grant create privilege to userZ whenever a new table created."), + HIVE_AUTHORIZATION_TABLE_GROUP_GRANTS("hive.security.authorization.createtable.group.grants", + "", + "the privileges automatically granted to some groups whenever a table gets created.\n" + + "An example like \"groupX,groupY:select;groupZ:create\" will grant select privilege to groupX and groupY,\n" + + "and grant create privilege to groupZ whenever a new table created."), + HIVE_AUTHORIZATION_TABLE_ROLE_GRANTS("hive.security.authorization.createtable.role.grants", "", + "the privileges automatically granted to some roles whenever a table gets created.\n" + + "An example like \"roleX,roleY:select;roleZ:create\" will grant select privilege to roleX and roleY,\n" + + "and grant create privilege to roleZ whenever a new table created."), + HIVE_AUTHORIZATION_TABLE_OWNER_GRANTS("hive.security.authorization.createtable.owner.grants", + "", + "The privileges automatically granted to the owner whenever a table gets created.\n" + + "An example like \"select,drop\" will grant select and drop privilege to the owner\n" + + "of the table. Note that the default gives the creator of a table no access to the\n" + + "table (but see HIVE-8067)."), + HIVE_AUTHORIZATION_TASK_FACTORY("hive.security.authorization.task.factory", + "org.apache.hadoop.hive.ql.parse.authorization.HiveAuthorizationTaskFactoryImpl", + "Authorization DDL task factory implementation"), + + // if this is not set default value is set during config initialization + // Default value can't be set in this constructor as it would refer names in other ConfVars + // whose constructor would not have been called + HIVE_AUTHORIZATION_SQL_STD_AUTH_CONFIG_WHITELIST( + "hive.security.authorization.sqlstd.confwhitelist", "", + "List of comma separated Java regexes. Configurations parameters that match these\n" + + "regexes can be modified by user when SQL standard authorization is enabled.\n" + + "To get the default value, use the 'set ' command.\n" + + "Note that the hive.conf.restricted.list checks are still enforced after the white list\n" + + "check"), + + HIVE_AUTHORIZATION_SQL_STD_AUTH_CONFIG_WHITELIST_APPEND( + "hive.security.authorization.sqlstd.confwhitelist.append", "", + "List of comma separated Java regexes, to be appended to list set in\n" + + "hive.security.authorization.sqlstd.confwhitelist. Using this list instead\n" + + "of updating the original list means that you can append to the defaults\n" + + "set by SQL standard authorization instead of replacing it entirely."), + + HIVE_CLI_PRINT_HEADER("hive.cli.print.header", false, "Whether to print the names of the columns in query output."), + + HIVE_CLI_TEZ_SESSION_ASYNC("hive.cli.tez.session.async", true, "Whether to start Tez\n" + + "session in background when running CLI with Tez, allowing CLI to be available earlier."), + + HIVE_ERROR_ON_EMPTY_PARTITION("hive.error.on.empty.partition", false, + "Whether to throw an exception if dynamic partition insert generates empty results."), + + HIVE_INDEX_COMPACT_FILE("hive.index.compact.file", "", "internal variable"), + HIVE_INDEX_BLOCKFILTER_FILE("hive.index.blockfilter.file", "", "internal variable"), + HIVE_INDEX_IGNORE_HDFS_LOC("hive.index.compact.file.ignore.hdfs", false, + "When true the HDFS location stored in the index file will be ignored at runtime.\n" + + "If the data got moved or the name of the cluster got changed, the index data should still be usable."), + + HIVE_EXIM_URI_SCHEME_WL("hive.exim.uri.scheme.whitelist", "hdfs,pfile", + "A comma separated list of acceptable URI schemes for import and export."), + // temporary variable for testing. This is added just to turn off this feature in case of a bug in + // deployment. It has not been documented in hive-default.xml intentionally, this should be removed + // once the feature is stable + HIVE_EXIM_RESTRICT_IMPORTS_INTO_REPLICATED_TABLES("hive.exim.strict.repl.tables",true, + "Parameter that determines if 'regular' (non-replication) export dumps can be\n" + + "imported on to tables that are the target of replication. If this parameter is\n" + + "set, regular imports will check if the destination table(if it exists) has a " + + "'repl.last.id' set on it. If so, it will fail."), + HIVE_REPL_TASK_FACTORY("hive.repl.task.factory", + "org.apache.hive.hcatalog.api.repl.exim.EximReplicationTaskFactory", + "Parameter that can be used to override which ReplicationTaskFactory will be\n" + + "used to instantiate ReplicationTask events. Override for third party repl plugins"), + HIVE_MAPPER_CANNOT_SPAN_MULTIPLE_PARTITIONS("hive.mapper.cannot.span.multiple.partitions", false, ""), + HIVE_REWORK_MAPREDWORK("hive.rework.mapredwork", false, + "should rework the mapred work or not.\n" + + "This is first introduced by SymlinkTextInputFormat to replace symlink files with real paths at compile time."), + HIVE_CONCATENATE_CHECK_INDEX ("hive.exec.concatenate.check.index", true, + "If this is set to true, Hive will throw error when doing\n" + + "'alter table tbl_name [partSpec] concatenate' on a table/partition\n" + + "that has indexes on it. The reason the user want to set this to true\n" + + "is because it can help user to avoid handling all index drop, recreation,\n" + + "rebuild work. This is very helpful for tables with thousands of partitions."), + HIVE_IO_EXCEPTION_HANDLERS("hive.io.exception.handlers", "", + "A list of io exception handler class names. This is used\n" + + "to construct a list exception handlers to handle exceptions thrown\n" + + "by record readers"), + + // logging configuration + HIVE_LOG4J_FILE("hive.log4j.file", "", + "Hive log4j configuration file.\n" + + "If the property is not set, then logging will be initialized using hive-log4j2.properties found on the classpath.\n" + + "If the property is set, the value must be a valid URI (java.net.URI, e.g. \"file:///tmp/my-logging.xml\"), \n" + + "which you can then extract a URL from and pass to PropertyConfigurator.configure(URL)."), + HIVE_EXEC_LOG4J_FILE("hive.exec.log4j.file", "", + "Hive log4j configuration file for execution mode(sub command).\n" + + "If the property is not set, then logging will be initialized using hive-exec-log4j2.properties found on the classpath.\n" + + "If the property is set, the value must be a valid URI (java.net.URI, e.g. \"file:///tmp/my-logging.xml\"), \n" + + "which you can then extract a URL from and pass to PropertyConfigurator.configure(URL)."), + + HIVE_LOG_EXPLAIN_OUTPUT("hive.log.explain.output", false, + "Whether to log explain output for every query.\n" + + "When enabled, will log EXPLAIN EXTENDED output for the query at INFO log4j log level."), + HIVE_EXPLAIN_USER("hive.explain.user", true, + "Whether to show explain result at user level.\n" + + "When enabled, will log EXPLAIN output for the query at user level."), + + // prefix used to auto generated column aliases (this should be started with '_') + HIVE_AUTOGEN_COLUMNALIAS_PREFIX_LABEL("hive.autogen.columnalias.prefix.label", "_c", + "String used as a prefix when auto generating column alias.\n" + + "By default the prefix label will be appended with a column position number to form the column alias. \n" + + "Auto generation would happen if an aggregate function is used in a select clause without an explicit alias."), + HIVE_AUTOGEN_COLUMNALIAS_PREFIX_INCLUDEFUNCNAME( + "hive.autogen.columnalias.prefix.includefuncname", false, + "Whether to include function name in the column alias auto generated by Hive."), + HIVE_METRICS_CLASS("hive.service.metrics.class", + "org.apache.hadoop.hive.common.metrics.metrics2.CodahaleMetrics", + new StringSet( + "org.apache.hadoop.hive.common.metrics.metrics2.CodahaleMetrics", + "org.apache.hadoop.hive.common.metrics.LegacyMetrics"), + "Hive metrics subsystem implementation class."), + HIVE_METRICS_REPORTER("hive.service.metrics.reporter", "JSON_FILE, JMX", + "Reporter type for metric class org.apache.hadoop.hive.common.metrics.metrics2.CodahaleMetrics, comma separated list of JMX, CONSOLE, JSON_FILE"), + HIVE_METRICS_JSON_FILE_LOCATION("hive.service.metrics.file.location", "/tmp/report.json", + "For metric class org.apache.hadoop.hive.common.metrics.metrics2.CodahaleMetrics JSON_FILE reporter, the location of local JSON metrics file. " + + "This file will get overwritten at every interval."), + HIVE_METRICS_JSON_FILE_INTERVAL("hive.service.metrics.file.frequency", "5s", + new TimeValidator(TimeUnit.MILLISECONDS), + "For metric class org.apache.hadoop.hive.common.metrics.metrics2.CodahaleMetrics JSON_FILE reporter, " + + "the frequency of updating JSON metrics file."), + HIVE_PERF_LOGGER("hive.exec.perf.logger", "org.apache.hadoop.hive.ql.log.PerfLogger", + "The class responsible for logging client side performance metrics. \n" + + "Must be a subclass of org.apache.hadoop.hive.ql.log.PerfLogger"), + HIVE_START_CLEANUP_SCRATCHDIR("hive.start.cleanup.scratchdir", false, + "To cleanup the Hive scratchdir when starting the Hive Server"), + HIVE_INSERT_INTO_MULTILEVEL_DIRS("hive.insert.into.multilevel.dirs", false, + "Where to insert into multilevel directories like\n" + + "\"insert directory '/HIVEFT25686/chinna/' from table\""), + HIVE_WAREHOUSE_SUBDIR_INHERIT_PERMS("hive.warehouse.subdir.inherit.perms", true, + "Set this to false if the table directories should be created\n" + + "with the permissions derived from dfs umask instead of\n" + + "inheriting the permission of the warehouse or database directory."), + HIVE_INSERT_INTO_EXTERNAL_TABLES("hive.insert.into.external.tables", true, + "whether insert into external tables is allowed"), + HIVE_TEMPORARY_TABLE_STORAGE( + "hive.exec.temporary.table.storage", "default", new StringSet("memory", + "ssd", "default"), "Define the storage policy for temporary tables." + + "Choices between memory, ssd and default"), + + HIVE_DRIVER_RUN_HOOKS("hive.exec.driver.run.hooks", "", + "A comma separated list of hooks which implement HiveDriverRunHook. Will be run at the beginning " + + "and end of Driver.run, these will be run in the order specified."), + HIVE_DDL_OUTPUT_FORMAT("hive.ddl.output.format", null, + "The data format to use for DDL output. One of \"text\" (for human\n" + + "readable text) or \"json\" (for a json object)."), + HIVE_ENTITY_SEPARATOR("hive.entity.separator", "@", + "Separator used to construct names of tables and partitions. For example, dbname@tablename@partitionname"), + HIVE_CAPTURE_TRANSFORM_ENTITY("hive.entity.capture.transform", false, + "Compiler to capture transform URI referred in the query"), + HIVE_DISPLAY_PARTITION_COLUMNS_SEPARATELY("hive.display.partition.cols.separately", true, + "In older Hive version (0.10 and earlier) no distinction was made between\n" + + "partition columns or non-partition columns while displaying columns in describe\n" + + "table. From 0.12 onwards, they are displayed separately. This flag will let you\n" + + "get old behavior, if desired. See, test-case in patch for HIVE-6689."), + + HIVE_SSL_PROTOCOL_BLACKLIST("hive.ssl.protocol.blacklist", "SSLv2,SSLv3", + "SSL Versions to disable for all Hive Servers"), + + // HiveServer2 specific configs + HIVE_SERVER2_MAX_START_ATTEMPTS("hive.server2.max.start.attempts", 30L, new RangeValidator(0L, null), + "Number of times HiveServer2 will attempt to start before exiting, sleeping 60 seconds " + + "between retries. \n The default of 30 will keep trying for 30 minutes."), + HIVE_SERVER2_SUPPORT_DYNAMIC_SERVICE_DISCOVERY("hive.server2.support.dynamic.service.discovery", false, + "Whether HiveServer2 supports dynamic service discovery for its clients. " + + "To support this, each instance of HiveServer2 currently uses ZooKeeper to register itself, " + + "when it is brought up. JDBC/ODBC clients should use the ZooKeeper ensemble: " + + "hive.zookeeper.quorum in their connection string."), + HIVE_SERVER2_ZOOKEEPER_NAMESPACE("hive.server2.zookeeper.namespace", "hiveserver2", + "The parent node in ZooKeeper used by HiveServer2 when supporting dynamic service discovery."), + + // HiveServer2 global init file location + HIVE_SERVER2_GLOBAL_INIT_FILE_LOCATION("hive.server2.global.init.file.location", "${env:HIVE_CONF_DIR}", + "Either the location of a HS2 global init file or a directory containing a .hiverc file. If the \n" + + "property is set, the value must be a valid path to an init file or directory where the init file is located."), + HIVE_SERVER2_TRANSPORT_MODE("hive.server2.transport.mode", "binary", new StringSet("binary", "http"), + "Transport mode of HiveServer2."), + HIVE_SERVER2_THRIFT_BIND_HOST("hive.server2.thrift.bind.host", "", + "Bind host on which to run the HiveServer2 Thrift service."), + HIVE_SERVER2_PARALLEL_COMPILATION("hive.driver.parallel.compilation", false, "Whether to\n" + + "enable parallel compilation between sessions on HiveServer2. The default is false."), + + // HiveServer2 WebUI + HIVE_SERVER2_WEBUI_BIND_HOST("hive.server2.webui.host", "0.0.0.0", "The host address the HiveServer2 WebUI will listen on"), + HIVE_SERVER2_WEBUI_PORT("hive.server2.webui.port", 10002, "The port the HiveServer2 WebUI will listen on"), + HIVE_SERVER2_WEBUI_MAX_THREADS("hive.server2.webui.max.threads", 50, "The max HiveServer2 WebUI threads"), + + // Tez session settings + HIVE_SERVER2_TEZ_DEFAULT_QUEUES("hive.server2.tez.default.queues", "", + "A list of comma separated values corresponding to YARN queues of the same name.\n" + + "When HiveServer2 is launched in Tez mode, this configuration needs to be set\n" + + "for multiple Tez sessions to run in parallel on the cluster."), + HIVE_SERVER2_TEZ_SESSIONS_PER_DEFAULT_QUEUE("hive.server2.tez.sessions.per.default.queue", 1, + "A positive integer that determines the number of Tez sessions that should be\n" + + "launched on each of the queues specified by \"hive.server2.tez.default.queues\".\n" + + "Determines the parallelism on each queue."), + HIVE_SERVER2_TEZ_INITIALIZE_DEFAULT_SESSIONS("hive.server2.tez.initialize.default.sessions", false, + "This flag is used in HiveServer2 to enable a user to use HiveServer2 without\n" + + "turning on Tez for HiveServer2. The user could potentially want to run queries\n" + + "over Tez without the pool of sessions."), + + // Operation log configuration + HIVE_SERVER2_LOGGING_OPERATION_ENABLED("hive.server2.logging.operation.enabled", true, + "When true, HS2 will save operation logs and make them available for clients"), + HIVE_SERVER2_LOGGING_OPERATION_LOG_LOCATION("hive.server2.logging.operation.log.location", + "${system:java.io.tmpdir}" + File.separator + "${system:user.name}" + File.separator + + "operation_logs", + "Top level directory where operation logs are stored if logging functionality is enabled"), + HIVE_SERVER2_LOGGING_OPERATION_LEVEL("hive.server2.logging.operation.level", "EXECUTION", + new StringSet("NONE", "EXECUTION", "PERFORMANCE", "VERBOSE"), + "HS2 operation logging mode available to clients to be set at session level.\n" + + "For this to work, hive.server2.logging.operation.enabled should be set to true.\n" + + " NONE: Ignore any logging\n" + + " EXECUTION: Log completion of tasks\n" + + " PERFORMANCE: Execution + Performance logs \n" + + " VERBOSE: All logs" ), + + // Enable metric collection for HiveServer2 + HIVE_SERVER2_METRICS_ENABLED("hive.server2.metrics.enabled", false, "Enable metrics on the HiveServer2."), + + // http (over thrift) transport settings + HIVE_SERVER2_THRIFT_HTTP_PORT("hive.server2.thrift.http.port", 10001, + "Port number of HiveServer2 Thrift interface when hive.server2.transport.mode is 'http'."), + HIVE_SERVER2_THRIFT_HTTP_PATH("hive.server2.thrift.http.path", "cliservice", + "Path component of URL endpoint when in HTTP mode."), + HIVE_SERVER2_THRIFT_MAX_MESSAGE_SIZE("hive.server2.thrift.max.message.size", 100*1024*1024, + "Maximum message size in bytes a HS2 server will accept."), + HIVE_SERVER2_THRIFT_HTTP_MAX_IDLE_TIME("hive.server2.thrift.http.max.idle.time", "1800s", + new TimeValidator(TimeUnit.MILLISECONDS), + "Maximum idle time for a connection on the server when in HTTP mode."), + HIVE_SERVER2_THRIFT_HTTP_WORKER_KEEPALIVE_TIME("hive.server2.thrift.http.worker.keepalive.time", "60s", + new TimeValidator(TimeUnit.SECONDS), + "Keepalive time for an idle http worker thread. When the number of workers exceeds min workers, " + + "excessive threads are killed after this time interval."), + HIVE_SERVER2_THRIFT_HTTP_REQUEST_HEADER_SIZE("hive.server2.thrift.http.request.header.size", 6*1024, + "Request header size in bytes, when using HTTP transport mode. Jetty defaults used."), + HIVE_SERVER2_THRIFT_HTTP_RESPONSE_HEADER_SIZE("hive.server2.thrift.http.response.header.size", 6*1024, + "Response header size in bytes, when using HTTP transport mode. Jetty defaults used."), + + // Cookie based authentication when using HTTP Transport + HIVE_SERVER2_THRIFT_HTTP_COOKIE_AUTH_ENABLED("hive.server2.thrift.http.cookie.auth.enabled", true, + "When true, HiveServer2 in HTTP transport mode, will use cookie based authentication mechanism."), + HIVE_SERVER2_THRIFT_HTTP_COOKIE_MAX_AGE("hive.server2.thrift.http.cookie.max.age", "86400s", + new TimeValidator(TimeUnit.SECONDS), + "Maximum age in seconds for server side cookie used by HS2 in HTTP mode."), + HIVE_SERVER2_THRIFT_HTTP_COOKIE_DOMAIN("hive.server2.thrift.http.cookie.domain", null, + "Domain for the HS2 generated cookies"), + HIVE_SERVER2_THRIFT_HTTP_COOKIE_PATH("hive.server2.thrift.http.cookie.path", null, + "Path for the HS2 generated cookies"), + HIVE_SERVER2_THRIFT_HTTP_COOKIE_IS_SECURE("hive.server2.thrift.http.cookie.is.secure", true, + "Secure attribute of the HS2 generated cookie."), + HIVE_SERVER2_THRIFT_HTTP_COOKIE_IS_HTTPONLY("hive.server2.thrift.http.cookie.is.httponly", true, + "HttpOnly attribute of the HS2 generated cookie."), + + // binary transport settings + HIVE_SERVER2_THRIFT_PORT("hive.server2.thrift.port", 10000, + "Port number of HiveServer2 Thrift interface when hive.server2.transport.mode is 'binary'."), + HIVE_SERVER2_THRIFT_SASL_QOP("hive.server2.thrift.sasl.qop", "auth", + new StringSet("auth", "auth-int", "auth-conf"), + "Sasl QOP value; set it to one of following values to enable higher levels of\n" + + "protection for HiveServer2 communication with clients.\n" + + "Setting hadoop.rpc.protection to a higher level than HiveServer2 does not\n" + + "make sense in most situations. HiveServer2 ignores hadoop.rpc.protection in favor\n" + + "of hive.server2.thrift.sasl.qop.\n" + + " \"auth\" - authentication only (default)\n" + + " \"auth-int\" - authentication plus integrity protection\n" + + " \"auth-conf\" - authentication plus integrity and confidentiality protection\n" + + "This is applicable only if HiveServer2 is configured to use Kerberos authentication."), + HIVE_SERVER2_THRIFT_MIN_WORKER_THREADS("hive.server2.thrift.min.worker.threads", 5, + "Minimum number of Thrift worker threads"), + HIVE_SERVER2_THRIFT_MAX_WORKER_THREADS("hive.server2.thrift.max.worker.threads", 500, + "Maximum number of Thrift worker threads"), + HIVE_SERVER2_THRIFT_LOGIN_BEBACKOFF_SLOT_LENGTH( + "hive.server2.thrift.exponential.backoff.slot.length", "100ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Binary exponential backoff slot time for Thrift clients during login to HiveServer2,\n" + + "for retries until hitting Thrift client timeout"), + HIVE_SERVER2_THRIFT_LOGIN_TIMEOUT("hive.server2.thrift.login.timeout", "20s", + new TimeValidator(TimeUnit.SECONDS), "Timeout for Thrift clients during login to HiveServer2"), + HIVE_SERVER2_THRIFT_WORKER_KEEPALIVE_TIME("hive.server2.thrift.worker.keepalive.time", "60s", + new TimeValidator(TimeUnit.SECONDS), + "Keepalive time (in seconds) for an idle worker thread. When the number of workers exceeds min workers, " + + "excessive threads are killed after this time interval."), + // Configuration for async thread pool in SessionManager + HIVE_SERVER2_ASYNC_EXEC_THREADS("hive.server2.async.exec.threads", 100, + "Number of threads in the async thread pool for HiveServer2"), + HIVE_SERVER2_ASYNC_EXEC_SHUTDOWN_TIMEOUT("hive.server2.async.exec.shutdown.timeout", "10s", + new TimeValidator(TimeUnit.SECONDS), + "How long HiveServer2 shutdown will wait for async threads to terminate."), + HIVE_SERVER2_ASYNC_EXEC_WAIT_QUEUE_SIZE("hive.server2.async.exec.wait.queue.size", 100, + "Size of the wait queue for async thread pool in HiveServer2.\n" + + "After hitting this limit, the async thread pool will reject new requests."), + HIVE_SERVER2_ASYNC_EXEC_KEEPALIVE_TIME("hive.server2.async.exec.keepalive.time", "10s", + new TimeValidator(TimeUnit.SECONDS), + "Time that an idle HiveServer2 async thread (from the thread pool) will wait for a new task\n" + + "to arrive before terminating"), + HIVE_SERVER2_LONG_POLLING_TIMEOUT("hive.server2.long.polling.timeout", "5000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Time that HiveServer2 will wait before responding to asynchronous calls that use long polling"), + + HIVE_SESSION_IMPL_CLASSNAME("hive.session.impl.classname", null, "Classname for custom implementation of hive session"), + HIVE_SESSION_IMPL_WITH_UGI_CLASSNAME("hive.session.impl.withugi.classname", null, "Classname for custom implementation of hive session with UGI"), + + // HiveServer2 auth configuration + HIVE_SERVER2_AUTHENTICATION("hive.server2.authentication", "NONE", + new StringSet("NOSASL", "NONE", "LDAP", "KERBEROS", "PAM", "CUSTOM"), + "Client authentication types.\n" + + " NONE: no authentication check\n" + + " LDAP: LDAP/AD based authentication\n" + + " KERBEROS: Kerberos/GSSAPI authentication\n" + + " CUSTOM: Custom authentication provider\n" + + " (Use with property hive.server2.custom.authentication.class)\n" + + " PAM: Pluggable authentication module\n" + + " NOSASL: Raw transport"), + HIVE_SERVER2_ALLOW_USER_SUBSTITUTION("hive.server2.allow.user.substitution", true, + "Allow alternate user to be specified as part of HiveServer2 open connection request."), + HIVE_SERVER2_KERBEROS_KEYTAB("hive.server2.authentication.kerberos.keytab", "", + "Kerberos keytab file for server principal"), + HIVE_SERVER2_KERBEROS_PRINCIPAL("hive.server2.authentication.kerberos.principal", "", + "Kerberos server principal"), + HIVE_SERVER2_SPNEGO_KEYTAB("hive.server2.authentication.spnego.keytab", "", + "keytab file for SPNego principal, optional,\n" + + "typical value would look like /etc/security/keytabs/spnego.service.keytab,\n" + + "This keytab would be used by HiveServer2 when Kerberos security is enabled and \n" + + "HTTP transport mode is used.\n" + + "This needs to be set only if SPNEGO is to be used in authentication.\n" + + "SPNego authentication would be honored only if valid\n" + + " hive.server2.authentication.spnego.principal\n" + + "and\n" + + " hive.server2.authentication.spnego.keytab\n" + + "are specified."), + HIVE_SERVER2_SPNEGO_PRINCIPAL("hive.server2.authentication.spnego.principal", "", + "SPNego service principal, optional,\n" + + "typical value would look like HTTP/_HOST@EXAMPLE.COM\n" + + "SPNego service principal would be used by HiveServer2 when Kerberos security is enabled\n" + + "and HTTP transport mode is used.\n" + + "This needs to be set only if SPNEGO is to be used in authentication."), + HIVE_SERVER2_PLAIN_LDAP_URL("hive.server2.authentication.ldap.url", null, + "LDAP connection URL(s),\n" + + "this value could contain URLs to mutiple LDAP servers instances for HA,\n" + + "each LDAP URL is separated by a SPACE character. URLs are used in the \n" + + " order specified until a connection is successful."), + HIVE_SERVER2_PLAIN_LDAP_BASEDN("hive.server2.authentication.ldap.baseDN", null, "LDAP base DN"), + HIVE_SERVER2_PLAIN_LDAP_DOMAIN("hive.server2.authentication.ldap.Domain", null, ""), + HIVE_SERVER2_PLAIN_LDAP_GROUPDNPATTERN("hive.server2.authentication.ldap.groupDNPattern", null, + "COLON-separated list of patterns to use to find DNs for group entities in this directory.\n" + + "Use %s where the actual group name is to be substituted for.\n" + + "For example: CN=%s,CN=Groups,DC=subdomain,DC=domain,DC=com."), + HIVE_SERVER2_PLAIN_LDAP_GROUPFILTER("hive.server2.authentication.ldap.groupFilter", null, + "COMMA-separated list of LDAP Group names (short name not full DNs).\n" + + "For example: HiveAdmins,HadoopAdmins,Administrators"), + HIVE_SERVER2_PLAIN_LDAP_USERDNPATTERN("hive.server2.authentication.ldap.userDNPattern", null, + "COLON-separated list of patterns to use to find DNs for users in this directory.\n" + + "Use %s where the actual group name is to be substituted for.\n" + + "For example: CN=%s,CN=Users,DC=subdomain,DC=domain,DC=com."), + HIVE_SERVER2_PLAIN_LDAP_USERFILTER("hive.server2.authentication.ldap.userFilter", null, + "COMMA-separated list of LDAP usernames (just short names, not full DNs).\n" + + "For example: hiveuser,impalauser,hiveadmin,hadoopadmin"), + HIVE_SERVER2_PLAIN_LDAP_CUSTOMLDAPQUERY("hive.server2.authentication.ldap.customLDAPQuery", null, + "A full LDAP query that LDAP Atn provider uses to execute against LDAP Server.\n" + + "If this query returns a null resultset, the LDAP Provider fails the Authentication\n" + + "request, succeeds if the user is part of the resultset." + + "For example: (&(objectClass=group)(objectClass=top)(instanceType=4)(cn=Domain*)) \n" + + "(&(objectClass=person)(|(sAMAccountName=admin)(|(memberOf=CN=Domain Admins,CN=Users,DC=domain,DC=com)" + + "(memberOf=CN=Administrators,CN=Builtin,DC=domain,DC=com))))"), + HIVE_SERVER2_CUSTOM_AUTHENTICATION_CLASS("hive.server2.custom.authentication.class", null, + "Custom authentication class. Used when property\n" + + "'hive.server2.authentication' is set to 'CUSTOM'. Provided class\n" + + "must be a proper implementation of the interface\n" + + "org.apache.hive.service.auth.PasswdAuthenticationProvider. HiveServer2\n" + + "will call its Authenticate(user, passed) method to authenticate requests.\n" + + "The implementation may optionally implement Hadoop's\n" + + "org.apache.hadoop.conf.Configurable class to grab Hive's Configuration object."), + HIVE_SERVER2_PAM_SERVICES("hive.server2.authentication.pam.services", null, + "List of the underlying pam services that should be used when auth type is PAM\n" + + "A file with the same name must exist in /etc/pam.d"), + + HIVE_SERVER2_ENABLE_DOAS("hive.server2.enable.doAs", true, + "Setting this property to true will have HiveServer2 execute\n" + + "Hive operations as the user making the calls to it."), + HIVE_SERVER2_TABLE_TYPE_MAPPING("hive.server2.table.type.mapping", "CLASSIC", new StringSet("CLASSIC", "HIVE"), + "This setting reflects how HiveServer2 will report the table types for JDBC and other\n" + + "client implementations that retrieve the available tables and supported table types\n" + + " HIVE : Exposes Hive's native table types like MANAGED_TABLE, EXTERNAL_TABLE, VIRTUAL_VIEW\n" + + " CLASSIC : More generic types like TABLE and VIEW"), + HIVE_SERVER2_SESSION_HOOK("hive.server2.session.hook", "", ""), + + // SSL settings + HIVE_SERVER2_USE_SSL("hive.server2.use.SSL", false, + "Set this to true for using SSL encryption in HiveServer2."), + HIVE_SERVER2_SSL_KEYSTORE_PATH("hive.server2.keystore.path", "", + "SSL certificate keystore location."), + HIVE_SERVER2_SSL_KEYSTORE_PASSWORD("hive.server2.keystore.password", "", + "SSL certificate keystore password."), + HIVE_SERVER2_MAP_FAIR_SCHEDULER_QUEUE("hive.server2.map.fair.scheduler.queue", true, + "If the YARN fair scheduler is configured and HiveServer2 is running in non-impersonation mode,\n" + + "this setting determines the user for fair scheduler queue mapping.\n" + + "If set to true (default), the logged-in user determines the fair scheduler queue\n" + + "for submitted jobs, so that map reduce resource usage can be tracked by user.\n" + + "If set to false, all Hive jobs go to the 'hive' user's queue."), + HIVE_SERVER2_BUILTIN_UDF_WHITELIST("hive.server2.builtin.udf.whitelist", "", + "Comma separated list of builtin udf names allowed in queries.\n" + + "An empty whitelist allows all builtin udfs to be executed. " + + " The udf black list takes precedence over udf white list"), + HIVE_SERVER2_BUILTIN_UDF_BLACKLIST("hive.server2.builtin.udf.blacklist", "", + "Comma separated list of udfs names. These udfs will not be allowed in queries." + + " The udf black list takes precedence over udf white list"), + + HIVE_SERVER2_SESSION_CHECK_INTERVAL("hive.server2.session.check.interval", "6h", + new TimeValidator(TimeUnit.MILLISECONDS, 3000l, true, null, false), + "The check interval for session/operation timeout, which can be disabled by setting to zero or negative value."), + HIVE_SERVER2_IDLE_SESSION_TIMEOUT("hive.server2.idle.session.timeout", "7d", + new TimeValidator(TimeUnit.MILLISECONDS), + "Session will be closed when it's not accessed for this duration, which can be disabled by setting to zero or negative value."), + HIVE_SERVER2_IDLE_OPERATION_TIMEOUT("hive.server2.idle.operation.timeout", "5d", + new TimeValidator(TimeUnit.MILLISECONDS), + "Operation will be closed when it's not accessed for this duration of time, which can be disabled by setting to zero value.\n" + + " With positive value, it's checked for operations in terminal state only (FINISHED, CANCELED, CLOSED, ERROR).\n" + + " With negative value, it's checked for all of the operations regardless of state."), + HIVE_SERVER2_IDLE_SESSION_CHECK_OPERATION("hive.server2.idle.session.check.operation", true, + "Session will be considered to be idle only if there is no activity, and there is no pending operation.\n" + + " This setting takes effect only if session idle timeout (hive.server2.idle.session.timeout) and checking\n" + + "(hive.server2.session.check.interval) are enabled."), + HIVE_SERVER2_THRIFT_CLIENT_RETRY_LIMIT("hive.server2.thrift.client.retry.limit", 1,"Number of retries upon " + + "failure of Thrift HiveServer2 calls"), + HIVE_SERVER2_THRIFT_CLIENT_CONNECTION_RETRY_LIMIT("hive.server2.thrift.client.connect.retry.limit", 1,"Number of " + + "retries while opening a connection to HiveServe2"), + HIVE_SERVER2_THRIFT_CLIENT_RETRY_DELAY_SECONDS("hive.server2.thrift.client.retry.delay.seconds", "1s", + new TimeValidator(TimeUnit.SECONDS), "Number of seconds for the HiveServer2 thrift client to wait between " + + "consecutive connection attempts. Also specifies the time to wait between retrying thrift calls upon failures"), + HIVE_SERVER2_THRIFT_CLIENT_USER("hive.server2.thrift.client.user", "anonymous","Username to use against thrift" + + " client"), + HIVE_SERVER2_THRIFT_CLIENT_PASSWORD("hive.server2.thrift.client.password", "anonymous","Password to use against " + + "thrift client"), + + HIVE_SECURITY_COMMAND_WHITELIST("hive.security.command.whitelist", "set,reset,dfs,add,list,delete,reload,compile", + "Comma separated list of non-SQL Hive commands users are authorized to execute"), + HIVE_CONF_RESTRICTED_LIST("hive.conf.restricted.list", + "hive.security.authenticator.manager,hive.security.authorization.manager,hive.users.in.admin.role", + "Comma separated list of configuration options which are immutable at runtime"), + HIVE_CONF_HIDDEN_LIST("hive.conf.hidden.list", + METASTOREPWD.varname + "," + HIVE_SERVER2_SSL_KEYSTORE_PASSWORD.varname, + "Comma separated list of configuration options which should not be read by normal user like passwords"), + + HIVE_CONF_INTERNAL_VARIABLE_LIST("hive.conf.internal.variable.list", + "hive.added.files.path,hive.added.jars.path,hive.added.archives.path", + "Comma separated list of variables which are used internally and should not be configurable."), + + // If this is set all move tasks at the end of a multi-insert query will only begin once all + // outputs are ready + HIVE_MULTI_INSERT_MOVE_TASKS_SHARE_DEPENDENCIES( + "hive.multi.insert.move.tasks.share.dependencies", false, + "If this is set all move tasks for tables/partitions (not directories) at the end of a\n" + + "multi-insert query will only begin once the dependencies for all these move tasks have been\n" + + "met.\n" + + "Advantages: If concurrency is enabled, the locks will only be released once the query has\n" + + " finished, so with this config enabled, the time when the table/partition is\n" + + " generated will be much closer to when the lock on it is released.\n" + + "Disadvantages: If concurrency is not enabled, with this disabled, the tables/partitions which\n" + + " are produced by this query and finish earlier will be available for querying\n" + + " much earlier. Since the locks are only released once the query finishes, this\n" + + " does not apply if concurrency is enabled."), + + HIVE_INFER_BUCKET_SORT("hive.exec.infer.bucket.sort", false, + "If this is set, when writing partitions, the metadata will include the bucketing/sorting\n" + + "properties with which the data was written if any (this will not overwrite the metadata\n" + + "inherited from the table if the table is bucketed/sorted)"), + + HIVE_INFER_BUCKET_SORT_NUM_BUCKETS_POWER_TWO( + "hive.exec.infer.bucket.sort.num.buckets.power.two", false, + "If this is set, when setting the number of reducers for the map reduce task which writes the\n" + + "final output files, it will choose a number which is a power of two, unless the user specifies\n" + + "the number of reducers to use using mapred.reduce.tasks. The number of reducers\n" + + "may be set to a power of two, only to be followed by a merge task meaning preventing\n" + + "anything from being inferred.\n" + + "With hive.exec.infer.bucket.sort set to true:\n" + + "Advantages: If this is not set, the number of buckets for partitions will seem arbitrary,\n" + + " which means that the number of mappers used for optimized joins, for example, will\n" + + " be very low. With this set, since the number of buckets used for any partition is\n" + + " a power of two, the number of mappers used for optimized joins will be the least\n" + + " number of buckets used by any partition being joined.\n" + + "Disadvantages: This may mean a much larger or much smaller number of reducers being used in the\n" + + " final map reduce job, e.g. if a job was originally going to take 257 reducers,\n" + + " it will now take 512 reducers, similarly if the max number of reducers is 511,\n" + + " and a job was going to use this many, it will now use 256 reducers."), + + HIVEOPTLISTBUCKETING("hive.optimize.listbucketing", false, + "Enable list bucketing optimizer. Default value is false so that we disable it by default."), + + // Allow TCP Keep alive socket option for for HiveServer or a maximum timeout for the socket. + SERVER_READ_SOCKET_TIMEOUT("hive.server.read.socket.timeout", "10s", + new TimeValidator(TimeUnit.SECONDS), + "Timeout for the HiveServer to close the connection if no response from the client. By default, 10 seconds."), + SERVER_TCP_KEEP_ALIVE("hive.server.tcp.keepalive", true, + "Whether to enable TCP keepalive for the Hive Server. Keepalive will prevent accumulation of half-open connections."), + + HIVE_DECODE_PARTITION_NAME("hive.decode.partition.name", false, + "Whether to show the unquoted partition names in query results."), + + HIVE_EXECUTION_ENGINE("hive.execution.engine", "mr", new StringSet("mr", "tez", "spark"), + "Chooses execution engine. Options are: mr (Map reduce, default), tez, spark. While MR\n" + + "remains the default engine for historical reasons, it is itself a historical engine\n" + + "and is deprecated in Hive 2 line. It may be removed without further warning."), + + HIVE_EXECUTION_MODE("hive.execution.mode", "container", new StringSet("container", "llap"), + "Chooses whether query fragments will run in container or in llap"), + + HIVE_JAR_DIRECTORY("hive.jar.directory", null, + "This is the location hive in tez mode will look for to find a site wide \n" + + "installed hive instance."), + HIVE_USER_INSTALL_DIR("hive.user.install.directory", "/user/", + "If hive (in tez mode only) cannot find a usable hive jar in \"hive.jar.directory\", \n" + + "it will upload the hive jar to \"hive.user.install.directory/user.name\"\n" + + "and use it to run queries."), + + // Vectorization enabled + HIVE_VECTORIZATION_ENABLED("hive.vectorized.execution.enabled", false, + "This flag should be set to true to enable vectorized mode of query execution.\n" + + "The default value is false."), + HIVE_VECTORIZATION_REDUCE_ENABLED("hive.vectorized.execution.reduce.enabled", true, + "This flag should be set to true to enable vectorized mode of the reduce-side of query execution.\n" + + "The default value is true."), + HIVE_VECTORIZATION_REDUCE_GROUPBY_ENABLED("hive.vectorized.execution.reduce.groupby.enabled", true, + "This flag should be set to true to enable vectorized mode of the reduce-side GROUP BY query execution.\n" + + "The default value is true."), + HIVE_VECTORIZATION_MAPJOIN_NATIVE_ENABLED("hive.vectorized.execution.mapjoin.native.enabled", true, + "This flag should be set to true to enable native (i.e. non-pass through) vectorization\n" + + "of queries using MapJoin.\n" + + "The default value is true."), + HIVE_VECTORIZATION_MAPJOIN_NATIVE_MULTIKEY_ONLY_ENABLED("hive.vectorized.execution.mapjoin.native.multikey.only.enabled", false, + "This flag should be set to true to restrict use of native vector map join hash tables to\n" + + "the MultiKey in queries using MapJoin.\n" + + "The default value is false."), + HIVE_VECTORIZATION_MAPJOIN_NATIVE_MINMAX_ENABLED("hive.vectorized.execution.mapjoin.minmax.enabled", false, + "This flag should be set to true to enable vector map join hash tables to\n" + + "use max / max filtering for integer join queries using MapJoin.\n" + + "The default value is false."), + HIVE_VECTORIZATION_MAPJOIN_NATIVE_OVERFLOW_REPEATED_THRESHOLD("hive.vectorized.execution.mapjoin.overflow.repeated.threshold", -1, + "The number of small table rows for a match in vector map join hash tables\n" + + "where we use the repeated field optimization in overflow vectorized row batch for join queries using MapJoin.\n" + + "A value of -1 means do use the join result optimization. Otherwise, threshold value can be 0 to maximum integer."), + HIVE_VECTORIZATION_MAPJOIN_NATIVE_FAST_HASHTABLE_ENABLED("hive.vectorized.execution.mapjoin.native.fast.hashtable.enabled", false, + "This flag should be set to true to enable use of native fast vector map join hash tables in\n" + + "queries using MapJoin.\n" + + "The default value is false."), + HIVE_VECTORIZATION_GROUPBY_CHECKINTERVAL("hive.vectorized.groupby.checkinterval", 100000, + "Number of entries added to the group by aggregation hash before a recomputation of average entry size is performed."), + HIVE_VECTORIZATION_GROUPBY_MAXENTRIES("hive.vectorized.groupby.maxentries", 1000000, + "Max number of entries in the vector group by aggregation hashtables. \n" + + "Exceeding this will trigger a flush irrelevant of memory pressure condition."), + HIVE_VECTORIZATION_GROUPBY_FLUSH_PERCENT("hive.vectorized.groupby.flush.percent", (float) 0.1, + "Percent of entries in the group by aggregation hash flushed when the memory threshold is exceeded."), + HIVE_VECTORIZATION_REDUCESINK_NEW_ENABLED("hive.vectorized.execution.reducesink.new.enabled", true, + "This flag should be set to true to enable the new vectorization\n" + + "of queries using ReduceSink.\ni" + + "The default value is true."), + HIVE_TYPE_CHECK_ON_INSERT("hive.typecheck.on.insert", true, "This property has been extended to control " + + "whether to check, convert, and normalize partition value to conform to its column type in " + + "partition operations including but not limited to insert, such as alter, describe etc."), + + HIVE_HADOOP_CLASSPATH("hive.hadoop.classpath", null, + "For Windows OS, we need to pass HIVE_HADOOP_CLASSPATH Java parameter while starting HiveServer2 \n" + + "using \"-hiveconf hive.hadoop.classpath=%HIVE_LIB%\"."), + + HIVE_RPC_QUERY_PLAN("hive.rpc.query.plan", false, + "Whether to send the query plan via local resource or RPC"), + HIVE_AM_SPLIT_GENERATION("hive.compute.splits.in.am", true, + "Whether to generate the splits locally or in the AM (tez only)"), + HIVE_TEZ_GENERATE_CONSISTENT_SPLITS("hive.tez.input.generate.consistent.splits", true, + "Whether to generate consistent split locations when generating splits in the AM"), + + HIVE_PREWARM_ENABLED("hive.prewarm.enabled", false, "Enables container prewarm for Tez/Spark (Hadoop 2 only)"), + HIVE_PREWARM_NUM_CONTAINERS("hive.prewarm.numcontainers", 10, "Controls the number of containers to prewarm for Tez/Spark (Hadoop 2 only)"), + + HIVESTAGEIDREARRANGE("hive.stageid.rearrange", "none", new StringSet("none", "idonly", "traverse", "execution"), ""), + HIVEEXPLAINDEPENDENCYAPPENDTASKTYPES("hive.explain.dependency.append.tasktype", false, ""), + + HIVECOUNTERGROUP("hive.counters.group.name", "HIVE", + "The name of counter group for internal Hive variables (CREATED_FILE, FATAL_ERROR, etc.)"), + + HIVE_QUOTEDID_SUPPORT("hive.support.quoted.identifiers", "column", + new StringSet("none", "column"), + "Whether to use quoted identifier. 'none' or 'column' can be used. \n" + + " none: default(past) behavior. Implies only alphaNumeric and underscore are valid characters in identifiers.\n" + + " column: implies column names can contain any character." + ), + HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS("hive.support.sql11.reserved.keywords", true, + "This flag should be set to true to enable support for SQL2011 reserved keywords.\n" + + "The default value is true."), + HIVE_SUPPORT_SPECICAL_CHARACTERS_IN_TABLE_NAMES("hive.support.special.characters.tablename", true, + "This flag should be set to true to enable support for special characters in table names.\n" + + "When it is set to false, only [a-zA-Z_0-9]+ are supported.\n" + + "The only supported special character right now is '/'. This flag applies only to quoted table names.\n" + + "The default value is true."), + // role names are case-insensitive + USERS_IN_ADMIN_ROLE("hive.users.in.admin.role", "", false, + "Comma separated list of users who are in admin role for bootstrapping.\n" + + "More users can be added in ADMIN role later."), + + HIVE_COMPAT("hive.compat", HiveCompat.DEFAULT_COMPAT_LEVEL, + "Enable (configurable) deprecated behaviors by setting desired level of backward compatibility.\n" + + "Setting to 0.12:\n" + + " Maintains division behavior: int / int = double"), + HIVE_CONVERT_JOIN_BUCKET_MAPJOIN_TEZ("hive.convert.join.bucket.mapjoin.tez", false, + "Whether joins can be automatically converted to bucket map joins in hive \n" + + "when tez is used as the execution engine."), + + HIVE_CHECK_CROSS_PRODUCT("hive.exec.check.crossproducts", true, + "Check if a plan contains a Cross Product. If there is one, output a warning to the Session's console."), + HIVE_LOCALIZE_RESOURCE_WAIT_INTERVAL("hive.localize.resource.wait.interval", "5000ms", + new TimeValidator(TimeUnit.MILLISECONDS), + "Time to wait for another thread to localize the same resource for hive-tez."), + HIVE_LOCALIZE_RESOURCE_NUM_WAIT_ATTEMPTS("hive.localize.resource.num.wait.attempts", 5, + "The number of attempts waiting for localizing a resource in hive-tez."), + TEZ_AUTO_REDUCER_PARALLELISM("hive.tez.auto.reducer.parallelism", false, + "Turn on Tez' auto reducer parallelism feature. When enabled, Hive will still estimate data sizes\n" + + "and set parallelism estimates. Tez will sample source vertices' output sizes and adjust the estimates at runtime as\n" + + "necessary."), + TEZ_MAX_PARTITION_FACTOR("hive.tez.max.partition.factor", 2f, + "When auto reducer parallelism is enabled this factor will be used to over-partition data in shuffle edges."), + TEZ_MIN_PARTITION_FACTOR("hive.tez.min.partition.factor", 0.25f, + "When auto reducer parallelism is enabled this factor will be used to put a lower limit to the number\n" + + "of reducers that tez specifies."), + TEZ_OPTIMIZE_BUCKET_PRUNING( + "hive.tez.bucket.pruning", false, + "When pruning is enabled, filters on bucket columns will be processed by \n" + + "filtering the splits against a bitset of included buckets. This needs predicates \n"+ + "produced by hive.optimize.ppd and hive.optimize.index.filters."), + TEZ_DYNAMIC_PARTITION_PRUNING( + "hive.tez.dynamic.partition.pruning", true, + "When dynamic pruning is enabled, joins on partition keys will be processed by sending\n" + + "events from the processing vertices to the Tez application master. These events will be\n" + + "used to prune unnecessary partitions."), + TEZ_DYNAMIC_PARTITION_PRUNING_MAX_EVENT_SIZE("hive.tez.dynamic.partition.pruning.max.event.size", 1*1024*1024L, + "Maximum size of events sent by processors in dynamic pruning. If this size is crossed no pruning will take place."), + + TEZ_DYNAMIC_PARTITION_PRUNING_MAX_DATA_SIZE("hive.tez.dynamic.partition.pruning.max.data.size", 100*1024*1024L, + "Maximum total data size of events in dynamic pruning."), + TEZ_SMB_NUMBER_WAVES( + "hive.tez.smb.number.waves", + (float) 0.5, + "The number of waves in which to run the SMB join. Account for cluster being occupied. Ideally should be 1 wave."), + TEZ_EXEC_SUMMARY( + "hive.tez.exec.print.summary", + false, + "Display breakdown of execution steps, for every query executed by the shell."), + TEZ_EXEC_INPLACE_PROGRESS( + "hive.tez.exec.inplace.progress", + true, + "Updates tez job execution progress in-place in the terminal."), + LLAP_IO_ENABLED("hive.llap.io.enabled", false, "Whether the LLAP IO layer is enabled."), + LLAP_LOW_LEVEL_CACHE("hive.llap.io.use.lowlevel.cache", true, "Must always be true for now"), + LLAP_ORC_CACHE_MIN_ALLOC("hive.llap.io.cache.orc.alloc.min", 128 * 1024, + "Minimum allocation possible from LLAP low-level cache for ORC. Allocations below that\n" + + "will be padded to minimum allocation. Should generally be the same as expected ORC\n" + + "compression buffer size, or next lowest power of 2. Must be power of 2."), + LLAP_ORC_CACHE_MAX_ALLOC("hive.llap.io.cache.orc.alloc.max", 16 * 1024 * 1024, + "Maximum allocation possible from LLAP low-level cache for ORC. Should be as large as\n" + + "the largest expected ORC compression buffer size. Must be power of 2."), + LLAP_ORC_CACHE_ARENA_COUNT("hive.llap.io.cache.orc.arena.count", 8, + "Arena count for LLAP low-level cache; cache will be allocated in the steps of\n" + + "(size/arena_count) bytes. This size must be <= 1Gb and >= max allocation; if it is\n" + + "not the case, an adjusted size will be used. Using powers of 2 is recommended."), + LLAP_ORC_CACHE_MAX_SIZE("hive.llap.io.cache.orc.size", 1024L * 1024 * 1024, + "Maximum size for ORC low-level cache; must be a multiple of arena size."), + LLAP_ORC_CACHE_ALLOCATE_DIRECT("hive.llap.io.cache.direct", true, + "Whether ORC low-level cache should use direct allocation."), + LLAP_USE_LRFU("hive.llap.io.use.lrfu", false, + "Whether ORC low-level cache should use LRFU cache policy instead of default (FIFO)."), + LLAP_LRFU_LAMBDA("hive.llap.io.lrfu.lambda", 0.01f, + "Lambda for ORC low-level cache LRFU cache policy. Must be in [0, 1]. 0 makes LRFU\n" + + "behave like LFU, 1 makes it behave like LRU, values in between balance accordingly."), + LLAP_ORC_ENABLE_TIME_COUNTERS("hive.llap.io.orc.time.counters", true, + "Whether to enable time counters for LLAP IO layer (time spent in HDFS, etc.)"), + LLAP_AUTO_ALLOW_UBER("hive.llap.auto.allow.uber", true, + "Whether or not to allow the planner to run vertices in the AM."), + LLAP_AUTO_ENFORCE_TREE("hive.llap.auto.enforce.tree", true, + "Enforce that all parents are in llap, before considering vertex"), + LLAP_AUTO_ENFORCE_VECTORIZED("hive.llap.auto.enforce.vectorized", true, + "Enforce that inputs are vectorized, before considering vertex"), + LLAP_AUTO_ENFORCE_STATS("hive.llap.auto.enforce.stats", true, + "Enforce that col stats are available, before considering vertex"), + LLAP_AUTO_MAX_INPUT("hive.llap.auto.max.input.size", 10*1024*1024*1024L, + "Check input size, before considering vertex (-1 disables check)"), + LLAP_AUTO_MAX_OUTPUT("hive.llap.auto.max.output.size", 1*1024*1024*1024L, + "Check output size, before considering vertex (-1 disables check)"), + LLAP_EXECUTION_MODE("hive.llap.execution.mode", "none", + new StringSet("auto", "none", "all", "map"), + "Chooses whether query fragments will run in container or in llap"), + LLAP_OBJECT_CACHE_ENABLED("hive.llap.object.cache.enabled", true, + "Cache objects (plans, hashtables, etc) in llap"), + LLAP_QUEUE_METRICS_PERCENTILE_INTERVALS("hive.llap.queue.metrics.percentiles.intervals", "", + "Comma-delimited set of integers denoting the desired rollover intervals (in seconds)\n" + + "for percentile latency metrics on the LLAP daemon producer-consumer queue.\n" + + "By default, percentile latency metrics are disabled."), + LLAP_IO_THREADPOOL_SIZE("hive.llap.io.threadpool.size", 10, + "Specify the number of threads to use for low-level IO thread pool."), + + + SPARK_CLIENT_FUTURE_TIMEOUT("hive.spark.client.future.timeout", + "60s", new TimeValidator(TimeUnit.SECONDS), + "Timeout for requests from Hive client to remote Spark driver."), + SPARK_JOB_MONITOR_TIMEOUT("hive.spark.job.monitor.timeout", + "60s", new TimeValidator(TimeUnit.SECONDS), + "Timeout for job monitor to get Spark job state."), + SPARK_RPC_CLIENT_CONNECT_TIMEOUT("hive.spark.client.connect.timeout", + "1000ms", new TimeValidator(TimeUnit.MILLISECONDS), + "Timeout for remote Spark driver in connecting back to Hive client."), + SPARK_RPC_CLIENT_HANDSHAKE_TIMEOUT("hive.spark.client.server.connect.timeout", + "90000ms", new TimeValidator(TimeUnit.MILLISECONDS), + "Timeout for handshake between Hive client and remote Spark driver. Checked by both processes."), + SPARK_RPC_SECRET_RANDOM_BITS("hive.spark.client.secret.bits", "256", + "Number of bits of randomness in the generated secret for communication between Hive client and remote Spark driver. " + + "Rounded down to the nearest multiple of 8."), + SPARK_RPC_MAX_THREADS("hive.spark.client.rpc.threads", 8, + "Maximum number of threads for remote Spark driver's RPC event loop."), + SPARK_RPC_MAX_MESSAGE_SIZE("hive.spark.client.rpc.max.size", 50 * 1024 * 1024, + "Maximum message size in bytes for communication between Hive client and remote Spark driver. Default is 50MB."), + SPARK_RPC_CHANNEL_LOG_LEVEL("hive.spark.client.channel.log.level", null, + "Channel logging level for remote Spark driver. One of {DEBUG, ERROR, INFO, TRACE, WARN}."), + SPARK_RPC_SASL_MECHANISM("hive.spark.client.rpc.sasl.mechanisms", "DIGEST-MD5", + "Name of the SASL mechanism to use for authentication."), + SPARK_DYNAMIC_PARTITION_PRUNING( + "hive.spark.dynamic.partition.pruning", false, + "When dynamic pruning is enabled, joins on partition keys will be processed by writing\n" + + "to a temporary HDFS file, and read later for removing unnecessary partitions."), + SPARK_DYNAMIC_PARTITION_PRUNING_MAX_DATA_SIZE( + "hive.spark.dynamic.partition.pruning.max.data.size", 100*1024*1024L, + "Maximum total data size in dynamic pruning."), + NWAYJOINREORDER("hive.reorder.nway.joins", true, + "Runs reordering of tables within single n-way join (i.e.: picks streamtable)"), + HIVE_LOG_N_RECORDS("hive.log.every.n.records", 0L, new RangeValidator(0L, null), + "If value is greater than 0 logs in fixed intervals of size n rather than exponentially."), + HIVE_MSCK_PATH_VALIDATION("hive.msck.path.validation", "throw", + new StringSet("throw", "skip", "ignore"), "The approach msck should take with HDFS " + + "directories that are partition-like but contain unsupported characters. 'throw' (an " + + "exception) is the default; 'skip' will skip the invalid directories and still repair the" + + " others; 'ignore' will skip the validation (legacy behavior, causes bugs in many cases)"), + HIVE_SERVER2_LLAP_CONCURRENT_QUERIES("hive.server2.llap.concurrent.queries", -1, + "The number of queries allowed in parallel via llap. Negative number implies 'infinite'."), + HIVE_TEZ_ENABLE_MEMORY_MANAGER("hive.tez.enable.memory.manager", true, + "Enable memory manager for tez"), + HIVE_HASH_TABLE_INFLATION_FACTOR("hive.hash.table.inflation.factor", (float) 2.0, + "Expected inflation factor between disk/in memory representation of hash tables"), + HIVE_LOG_TRACE_ID("hive.log.trace.id", "", + "Log tracing id that can be used by upstream clients for tracking respective logs. " + + "Truncated to " + LOG_PREFIX_LENGTH + " characters. Defaults to use auto-generated session id."); + + + public final String varname; + private final String defaultExpr; + + public final String defaultStrVal; + public final int defaultIntVal; + public final long defaultLongVal; + public final float defaultFloatVal; + public final boolean defaultBoolVal; + + private final Class valClass; + private final VarType valType; + + private final Validator validator; + + private final String description; + + private final boolean excluded; + private final boolean caseSensitive; + + ConfVars(String varname, Object defaultVal, String description) { + this(varname, defaultVal, null, description, true, false); + } + + ConfVars(String varname, Object defaultVal, String description, boolean excluded) { + this(varname, defaultVal, null, description, true, excluded); + } + + ConfVars(String varname, String defaultVal, boolean caseSensitive, String description) { + this(varname, defaultVal, null, description, caseSensitive, false); + } + + ConfVars(String varname, Object defaultVal, Validator validator, String description) { + this(varname, defaultVal, validator, description, true, false); + } + + ConfVars(String varname, Object defaultVal, Validator validator, String description, boolean caseSensitive, boolean excluded) { + this.varname = varname; + this.validator = validator; + this.description = description; + this.defaultExpr = defaultVal == null ? null : String.valueOf(defaultVal); + this.excluded = excluded; + this.caseSensitive = caseSensitive; + if (defaultVal == null || defaultVal instanceof String) { + this.valClass = String.class; + this.valType = VarType.STRING; + this.defaultStrVal = SystemVariables.substitute((String)defaultVal); + this.defaultIntVal = -1; + this.defaultLongVal = -1; + this.defaultFloatVal = -1; + this.defaultBoolVal = false; + } else if (defaultVal instanceof Integer) { + this.valClass = Integer.class; + this.valType = VarType.INT; + this.defaultStrVal = null; + this.defaultIntVal = (Integer)defaultVal; + this.defaultLongVal = -1; + this.defaultFloatVal = -1; + this.defaultBoolVal = false; + } else if (defaultVal instanceof Long) { + this.valClass = Long.class; + this.valType = VarType.LONG; + this.defaultStrVal = null; + this.defaultIntVal = -1; + this.defaultLongVal = (Long)defaultVal; + this.defaultFloatVal = -1; + this.defaultBoolVal = false; + } else if (defaultVal instanceof Float) { + this.valClass = Float.class; + this.valType = VarType.FLOAT; + this.defaultStrVal = null; + this.defaultIntVal = -1; + this.defaultLongVal = -1; + this.defaultFloatVal = (Float)defaultVal; + this.defaultBoolVal = false; + } else if (defaultVal instanceof Boolean) { + this.valClass = Boolean.class; + this.valType = VarType.BOOLEAN; + this.defaultStrVal = null; + this.defaultIntVal = -1; + this.defaultLongVal = -1; + this.defaultFloatVal = -1; + this.defaultBoolVal = (Boolean)defaultVal; + } else { + throw new IllegalArgumentException("Not supported type value " + defaultVal.getClass() + + " for name " + varname); + } + } + + public boolean isType(String value) { + return valType.isType(value); + } + + public Validator getValidator() { + return validator; + } + + public String validate(String value) { + return validator == null ? null : validator.validate(value); + } + + public String validatorDescription() { + return validator == null ? null : validator.toDescription(); + } + + public String typeString() { + String type = valType.typeString(); + if (valType == VarType.STRING && validator != null) { + if (validator instanceof TimeValidator) { + type += "(TIME)"; + } + } + return type; + } + + public String getRawDescription() { + return description; + } + + public String getDescription() { + String validator = validatorDescription(); + if (validator != null) { + return validator + ".\n" + description; + } + return description; + } + + public boolean isExcluded() { + return excluded; + } + + public boolean isCaseSensitive() { + return caseSensitive; + } + + @Override + public String toString() { + return varname; + } + + private static String findHadoopBinary() { + String val = System.getenv("HADOOP_HOME"); + // In Hadoop 1.X and Hadoop 2.X HADOOP_HOME is gone and replaced with HADOOP_PREFIX + if (val == null) { + val = System.getenv("HADOOP_PREFIX"); + } + // and if all else fails we can at least try /usr/bin/hadoop + val = (val == null ? File.separator + "usr" : val) + + File.separator + "bin" + File.separator + "hadoop"; + // Launch hadoop command file on windows. + return val + (Shell.WINDOWS ? ".cmd" : ""); + } + + public String getDefaultValue() { + return valType.defaultValueString(this); + } + + public String getDefaultExpr() { + return defaultExpr; + } + + private Set getValidStringValues() { + if (validator == null || !(validator instanceof StringSet)) { + throw new RuntimeException(varname + " does not specify a list of valid values"); + } + return ((StringSet)validator).getExpected(); + } + + enum VarType { + STRING { + @Override + void checkType(String value) throws Exception { } + @Override + String defaultValueString(ConfVars confVar) { return confVar.defaultStrVal; } + }, + INT { + @Override + void checkType(String value) throws Exception { Integer.valueOf(value); } + }, + LONG { + @Override + void checkType(String value) throws Exception { Long.valueOf(value); } + }, + FLOAT { + @Override + void checkType(String value) throws Exception { Float.valueOf(value); } + }, + BOOLEAN { + @Override + void checkType(String value) throws Exception { Boolean.valueOf(value); } + }; + + boolean isType(String value) { + try { checkType(value); } catch (Exception e) { return false; } + return true; + } + String typeString() { return name().toUpperCase();} + String defaultValueString(ConfVars confVar) { return confVar.defaultExpr; } + abstract void checkType(String value) throws Exception; + } + } + + /** + * Writes the default ConfVars out to a byte array and returns an input + * stream wrapping that byte array. + * + * We need this in order to initialize the ConfVar properties + * in the underling Configuration object using the addResource(InputStream) + * method. + * + * It is important to use a LoopingByteArrayInputStream because it turns out + * addResource(InputStream) is broken since Configuration tries to read the + * entire contents of the same InputStream repeatedly without resetting it. + * LoopingByteArrayInputStream has special logic to handle this. + */ + private static synchronized InputStream getConfVarInputStream() { + if (confVarByteArray == null) { + try { + // Create a Hadoop configuration without inheriting default settings. + Configuration conf = new Configuration(false); + + applyDefaultNonNullConfVars(conf); + + ByteArrayOutputStream confVarBaos = new ByteArrayOutputStream(); + conf.writeXml(confVarBaos); + confVarByteArray = confVarBaos.toByteArray(); + } catch (Exception e) { + // We're pretty screwed if we can't load the default conf vars + throw new RuntimeException("Failed to initialize default Hive configuration variables!", e); + } + } + return new LoopingByteArrayInputStream(confVarByteArray); + } + + public void verifyAndSet(String name, String value) throws IllegalArgumentException { + if (modWhiteListPattern != null) { + Matcher wlMatcher = modWhiteListPattern.matcher(name); + if (!wlMatcher.matches()) { + throw new IllegalArgumentException("Cannot modify " + name + " at runtime. " + + "It is not in list of params that are allowed to be modified at runtime"); + } + } + if (restrictList.contains(name)) { + throw new IllegalArgumentException("Cannot modify " + name + " at runtime. It is in the list" + + " of parameters that can't be modified at runtime"); + } + String oldValue = name != null ? get(name) : null; + if (name == null || value == null || !value.equals(oldValue)) { + // When either name or value is null, the set method below will fail, + // and throw IllegalArgumentException + set(name, value); + isSparkConfigUpdated = isSparkRelatedConfig(name); + } + } + + public boolean isHiddenConfig(String name) { + return hiddenSet.contains(name); + } + + /** + * check whether spark related property is updated, which includes spark configurations, + * RSC configurations and yarn configuration in Spark on YARN mode. + * @param name + * @return + */ + private boolean isSparkRelatedConfig(String name) { + boolean result = false; + if (name.startsWith("spark")) { // Spark property. + result = true; + } else if (name.startsWith("yarn")) { // YARN property in Spark on YARN mode. + String sparkMaster = get("spark.master"); + if (sparkMaster != null && + (sparkMaster.equals("yarn-client") || sparkMaster.equals("yarn-cluster"))) { + result = true; + } + } else if (name.startsWith("hive.spark")) { // Remote Spark Context property. + result = true; + } + + return result; + } + + public static int getIntVar(Configuration conf, ConfVars var) { + assert (var.valClass == Integer.class) : var.varname; + return conf.getInt(var.varname, var.defaultIntVal); + } + + public static void setIntVar(Configuration conf, ConfVars var, int val) { + assert (var.valClass == Integer.class) : var.varname; + conf.setInt(var.varname, val); + } + + public int getIntVar(ConfVars var) { + return getIntVar(this, var); + } + + public void setIntVar(ConfVars var, int val) { + setIntVar(this, var, val); + } + + public static long getTimeVar(Configuration conf, ConfVars var, TimeUnit outUnit) { + return toTime(getVar(conf, var), getDefaultTimeUnit(var), outUnit); + } + + public static void setTimeVar(Configuration conf, ConfVars var, long time, TimeUnit timeunit) { + assert (var.valClass == String.class) : var.varname; + conf.set(var.varname, time + stringFor(timeunit)); + } + + public long getTimeVar(ConfVars var, TimeUnit outUnit) { + return getTimeVar(this, var, outUnit); + } + + public void setTimeVar(ConfVars var, long time, TimeUnit outUnit) { + setTimeVar(this, var, time, outUnit); + } + + private static TimeUnit getDefaultTimeUnit(ConfVars var) { + TimeUnit inputUnit = null; + if (var.validator instanceof TimeValidator) { + inputUnit = ((TimeValidator)var.validator).getTimeUnit(); + } + return inputUnit; + } + + public static long toTime(String value, TimeUnit inputUnit, TimeUnit outUnit) { + String[] parsed = parseTime(value.trim()); + return outUnit.convert(Long.valueOf(parsed[0].trim().trim()), unitFor(parsed[1].trim(), inputUnit)); + } + + private static String[] parseTime(String value) { + char[] chars = value.toCharArray(); + int i = 0; + for (; i < chars.length && (chars[i] == '-' || Character.isDigit(chars[i])); i++) { + } + return new String[] {value.substring(0, i), value.substring(i)}; + } + + public static TimeUnit unitFor(String unit, TimeUnit defaultUnit) { + unit = unit.trim().toLowerCase(); + if (unit.isEmpty() || unit.equals("l")) { + if (defaultUnit == null) { + throw new IllegalArgumentException("Time unit is not specified"); + } + return defaultUnit; + } else if (unit.equals("d") || unit.startsWith("day")) { + return TimeUnit.DAYS; + } else if (unit.equals("h") || unit.startsWith("hour")) { + return TimeUnit.HOURS; + } else if (unit.equals("m") || unit.startsWith("min")) { + return TimeUnit.MINUTES; + } else if (unit.equals("s") || unit.startsWith("sec")) { + return TimeUnit.SECONDS; + } else if (unit.equals("ms") || unit.startsWith("msec")) { + return TimeUnit.MILLISECONDS; + } else if (unit.equals("us") || unit.startsWith("usec")) { + return TimeUnit.MICROSECONDS; + } else if (unit.equals("ns") || unit.startsWith("nsec")) { + return TimeUnit.NANOSECONDS; + } + throw new IllegalArgumentException("Invalid time unit " + unit); + } + + public static String stringFor(TimeUnit timeunit) { + switch (timeunit) { + case DAYS: return "day"; + case HOURS: return "hour"; + case MINUTES: return "min"; + case SECONDS: return "sec"; + case MILLISECONDS: return "msec"; + case MICROSECONDS: return "usec"; + case NANOSECONDS: return "nsec"; + } + throw new IllegalArgumentException("Invalid timeunit " + timeunit); + } + + public static long getLongVar(Configuration conf, ConfVars var) { + assert (var.valClass == Long.class) : var.varname; + return conf.getLong(var.varname, var.defaultLongVal); + } + + public static long getLongVar(Configuration conf, ConfVars var, long defaultVal) { + return conf.getLong(var.varname, defaultVal); + } + + public static void setLongVar(Configuration conf, ConfVars var, long val) { + assert (var.valClass == Long.class) : var.varname; + conf.setLong(var.varname, val); + } + + public long getLongVar(ConfVars var) { + return getLongVar(this, var); + } + + public void setLongVar(ConfVars var, long val) { + setLongVar(this, var, val); + } + + public static float getFloatVar(Configuration conf, ConfVars var) { + assert (var.valClass == Float.class) : var.varname; + return conf.getFloat(var.varname, var.defaultFloatVal); + } + + public static float getFloatVar(Configuration conf, ConfVars var, float defaultVal) { + return conf.getFloat(var.varname, defaultVal); + } + + public static void setFloatVar(Configuration conf, ConfVars var, float val) { + assert (var.valClass == Float.class) : var.varname; + conf.setFloat(var.varname, val); + } + + public float getFloatVar(ConfVars var) { + return getFloatVar(this, var); + } + + public void setFloatVar(ConfVars var, float val) { + setFloatVar(this, var, val); + } + + public static boolean getBoolVar(Configuration conf, ConfVars var) { + assert (var.valClass == Boolean.class) : var.varname; + return conf.getBoolean(var.varname, var.defaultBoolVal); + } + + public static boolean getBoolVar(Configuration conf, ConfVars var, boolean defaultVal) { + return conf.getBoolean(var.varname, defaultVal); + } + + public static void setBoolVar(Configuration conf, ConfVars var, boolean val) { + assert (var.valClass == Boolean.class) : var.varname; + conf.setBoolean(var.varname, val); + } + + public boolean getBoolVar(ConfVars var) { + return getBoolVar(this, var); + } + + public void setBoolVar(ConfVars var, boolean val) { + setBoolVar(this, var, val); + } + + public static String getVar(Configuration conf, ConfVars var) { + assert (var.valClass == String.class) : var.varname; + return conf.get(var.varname, var.defaultStrVal); + } + + public static String getVar(Configuration conf, ConfVars var, String defaultVal) { + return conf.get(var.varname, defaultVal); + } + + public String getLogIdVar(String defaultValue) { + String retval = getVar(ConfVars.HIVE_LOG_TRACE_ID); + if (retval.equals("")) { + l4j.info("Using the default value passed in for log id: " + defaultValue); + retval = defaultValue; + } + if (retval.length() > LOG_PREFIX_LENGTH) { + l4j.warn("The original log id prefix is " + retval + " has been truncated to " + + retval.substring(0, LOG_PREFIX_LENGTH - 1)); + retval = retval.substring(0, LOG_PREFIX_LENGTH - 1); + } + return retval; + } + + public static void setVar(Configuration conf, ConfVars var, String val) { + assert (var.valClass == String.class) : var.varname; + conf.set(var.varname, val); + } + + public static ConfVars getConfVars(String name) { + return vars.get(name); + } + + public static ConfVars getMetaConf(String name) { + return metaConfs.get(name); + } + + public String getVar(ConfVars var) { + return getVar(this, var); + } + + public void setVar(ConfVars var, String val) { + setVar(this, var, val); + } + + public void logVars(PrintStream ps) { + for (ConfVars one : ConfVars.values()) { + ps.println(one.varname + "=" + ((get(one.varname) != null) ? get(one.varname) : "")); + } + } + + public HiveConf() { + super(); + initialize(this.getClass()); + } + + public HiveConf(Class cls) { + super(); + initialize(cls); + } + + public HiveConf(Configuration other, Class cls) { + super(other); + initialize(cls); + } + + /** + * Copy constructor + */ + public HiveConf(HiveConf other) { + super(other); + hiveJar = other.hiveJar; + auxJars = other.auxJars; + isSparkConfigUpdated = other.isSparkConfigUpdated; + origProp = (Properties)other.origProp.clone(); + restrictList.addAll(other.restrictList); + hiddenSet.addAll(other.hiddenSet); + modWhiteListPattern = other.modWhiteListPattern; + } + + public Properties getAllProperties() { + return getProperties(this); + } + + public static Properties getProperties(Configuration conf) { + Iterator> iter = conf.iterator(); + Properties p = new Properties(); + while (iter.hasNext()) { + Map.Entry e = iter.next(); + p.setProperty(e.getKey(), e.getValue()); + } + return p; + } + + private void initialize(Class cls) { + hiveJar = (new JobConf(cls)).getJar(); + + // preserve the original configuration + origProp = getAllProperties(); + + // Overlay the ConfVars. Note that this ignores ConfVars with null values + addResource(getConfVarInputStream()); + + // Overlay hive-site.xml if it exists + if (hiveSiteURL != null) { + addResource(hiveSiteURL); + } + + // if embedded metastore is to be used as per config so far + // then this is considered like the metastore server case + String msUri = this.getVar(HiveConf.ConfVars.METASTOREURIS); + if(HiveConfUtil.isEmbeddedMetaStore(msUri)){ + setLoadMetastoreConfig(true); + } + + // load hivemetastore-site.xml if this is metastore and file exists + if (isLoadMetastoreConfig() && hivemetastoreSiteUrl != null) { + addResource(hivemetastoreSiteUrl); + } + + // load hiveserver2-site.xml if this is hiveserver2 and file exists + // metastore can be embedded within hiveserver2, in such cases + // the conf params in hiveserver2-site.xml will override whats defined + // in hivemetastore-site.xml + if (isLoadHiveServer2Config() && hiveServer2SiteUrl != null) { + addResource(hiveServer2SiteUrl); + } + + // Overlay the values of any system properties whose names appear in the list of ConfVars + applySystemProperties(); + + if ((this.get("hive.metastore.ds.retry.attempts") != null) || + this.get("hive.metastore.ds.retry.interval") != null) { + l4j.warn("DEPRECATED: hive.metastore.ds.retry.* no longer has any effect. " + + "Use hive.hmshandler.retry.* instead"); + } + + // if the running class was loaded directly (through eclipse) rather than through a + // jar then this would be needed + if (hiveJar == null) { + hiveJar = this.get(ConfVars.HIVEJAR.varname); + } + + if (auxJars == null) { + auxJars = this.get(ConfVars.HIVEAUXJARS.varname); + } + + if (getBoolVar(ConfVars.METASTORE_SCHEMA_VERIFICATION)) { + setBoolVar(ConfVars.METASTORE_AUTO_CREATE_SCHEMA, false); + setBoolVar(ConfVars.METASTORE_FIXED_DATASTORE, true); + } + + if (getBoolVar(HiveConf.ConfVars.HIVECONFVALIDATION)) { + List trimmed = new ArrayList(); + for (Map.Entry entry : this) { + String key = entry.getKey(); + if (key == null || !key.startsWith("hive.")) { + continue; + } + ConfVars var = HiveConf.getConfVars(key); + if (var == null) { + var = HiveConf.getConfVars(key.trim()); + if (var != null) { + trimmed.add(key); + } + } + if (var == null) { + l4j.warn("HiveConf of name " + key + " does not exist"); + } else if (!var.isType(entry.getValue())) { + l4j.warn("HiveConf " + var.varname + " expects " + var.typeString() + " type value"); + } + } + for (String key : trimmed) { + set(key.trim(), getRaw(key)); + unset(key); + } + } + + setupSQLStdAuthWhiteList(); + + // setup list of conf vars that are not allowed to change runtime + setupRestrictList(); + setupHiddenSet(); + + } + + /** + * If the config whitelist param for sql standard authorization is not set, set it up here. + */ + private void setupSQLStdAuthWhiteList() { + String whiteListParamsStr = getVar(ConfVars.HIVE_AUTHORIZATION_SQL_STD_AUTH_CONFIG_WHITELIST); + if (whiteListParamsStr == null || whiteListParamsStr.trim().isEmpty()) { + // set the default configs in whitelist + whiteListParamsStr = getSQLStdAuthDefaultWhiteListPattern(); + } + setVar(ConfVars.HIVE_AUTHORIZATION_SQL_STD_AUTH_CONFIG_WHITELIST, whiteListParamsStr); + } + + private static String getSQLStdAuthDefaultWhiteListPattern() { + // create the default white list from list of safe config params + // and regex list + String confVarPatternStr = Joiner.on("|").join(convertVarsToRegex(sqlStdAuthSafeVarNames)); + String regexPatternStr = Joiner.on("|").join(sqlStdAuthSafeVarNameRegexes); + return regexPatternStr + "|" + confVarPatternStr; + } + + /** + * @param paramList list of parameter strings + * @return list of parameter strings with "." replaced by "\." + */ + private static String[] convertVarsToRegex(String[] paramList) { + String[] regexes = new String[paramList.length]; + for(int i=0; i systemProperties = getConfSystemProperties(); + for (Entry systemProperty : systemProperties.entrySet()) { + this.set(systemProperty.getKey(), systemProperty.getValue()); + } + } + + /** + * This method returns a mapping from config variable name to its value for all config variables + * which have been set using System properties + */ + public static Map getConfSystemProperties() { + Map systemProperties = new HashMap(); + + for (ConfVars oneVar : ConfVars.values()) { + if (System.getProperty(oneVar.varname) != null) { + if (System.getProperty(oneVar.varname).length() > 0) { + systemProperties.put(oneVar.varname, System.getProperty(oneVar.varname)); + } + } + } + + return systemProperties; + } + + /** + * Overlays ConfVar properties with non-null values + */ + private static void applyDefaultNonNullConfVars(Configuration conf) { + for (ConfVars var : ConfVars.values()) { + String defaultValue = var.getDefaultValue(); + if (defaultValue == null) { + // Don't override ConfVars with null values + continue; + } + conf.set(var.varname, defaultValue); + } + } + + public Properties getChangedProperties() { + Properties ret = new Properties(); + Properties newProp = getAllProperties(); + + for (Object one : newProp.keySet()) { + String oneProp = (String) one; + String oldValue = origProp.getProperty(oneProp); + if (!StringUtils.equals(oldValue, newProp.getProperty(oneProp))) { + ret.setProperty(oneProp, newProp.getProperty(oneProp)); + } + } + return (ret); + } + + public String getJar() { + return hiveJar; + } + + /** + * @return the auxJars + */ + public String getAuxJars() { + return auxJars; + } + + /** + * @param auxJars the auxJars to set + */ + public void setAuxJars(String auxJars) { + this.auxJars = auxJars; + setVar(this, ConfVars.HIVEAUXJARS, auxJars); + } + + public URL getHiveDefaultLocation() { + return hiveDefaultURL; + } + + public static void setHiveSiteLocation(URL location) { + hiveSiteURL = location; + } + + public static URL getHiveSiteLocation() { + return hiveSiteURL; + } + + public static URL getMetastoreSiteLocation() { + return hivemetastoreSiteUrl; + } + + public static URL getHiveServer2SiteLocation() { + return hiveServer2SiteUrl; + } + + /** + * @return the user name set in hadoop.job.ugi param or the current user from System + * @throws IOException + */ + public String getUser() throws IOException { + try { + UserGroupInformation ugi = Utils.getUGI(); + return ugi.getUserName(); + } catch (LoginException le) { + throw new IOException(le); + } + } + + public static String getColumnInternalName(int pos) { + return "_col" + pos; + } + + public static int getPositionFromInternalName(String internalName) { + Pattern internalPattern = Pattern.compile("_col([0-9]+)"); + Matcher m = internalPattern.matcher(internalName); + if (!m.matches()){ + return -1; + } else { + return Integer.parseInt(m.group(1)); + } + } + + /** + * Append comma separated list of config vars to the restrict List + * @param restrictListStr + */ + public void addToRestrictList(String restrictListStr) { + if (restrictListStr == null) { + return; + } + String oldList = this.getVar(ConfVars.HIVE_CONF_RESTRICTED_LIST); + if (oldList == null || oldList.isEmpty()) { + this.setVar(ConfVars.HIVE_CONF_RESTRICTED_LIST, restrictListStr); + } else { + this.setVar(ConfVars.HIVE_CONF_RESTRICTED_LIST, oldList + "," + restrictListStr); + } + setupRestrictList(); + } + + /** + * Set white list of parameters that are allowed to be modified + * + * @param paramNameRegex + */ + @LimitedPrivate(value = { "Currently only for use by HiveAuthorizer" }) + public void setModifiableWhiteListRegex(String paramNameRegex) { + if (paramNameRegex == null) { + return; + } + modWhiteListPattern = Pattern.compile(paramNameRegex); + } + + /** + * Add the HIVE_CONF_RESTRICTED_LIST values to restrictList, + * including HIVE_CONF_RESTRICTED_LIST itself + */ + private void setupRestrictList() { + String restrictListStr = this.getVar(ConfVars.HIVE_CONF_RESTRICTED_LIST); + restrictList.clear(); + if (restrictListStr != null) { + for (String entry : restrictListStr.split(",")) { + restrictList.add(entry.trim()); + } + } + + String internalVariableListStr = this.getVar(ConfVars.HIVE_CONF_INTERNAL_VARIABLE_LIST); + if (internalVariableListStr != null) { + for (String entry : internalVariableListStr.split(",")) { + restrictList.add(entry.trim()); + } + } + + restrictList.add(ConfVars.HIVE_IN_TEST.varname); + restrictList.add(ConfVars.HIVE_CONF_RESTRICTED_LIST.varname); + restrictList.add(ConfVars.HIVE_CONF_HIDDEN_LIST.varname); + restrictList.add(ConfVars.HIVE_CONF_INTERNAL_VARIABLE_LIST.varname); + } + + private void setupHiddenSet() { + String hiddenListStr = this.getVar(ConfVars.HIVE_CONF_HIDDEN_LIST); + hiddenSet.clear(); + if (hiddenListStr != null) { + for (String entry : hiddenListStr.split(",")) { + hiddenSet.add(entry.trim()); + } + } + } + + /** + * Strips hidden config entries from configuration + */ + public void stripHiddenConfigurations(Configuration conf) { + for (String name : hiddenSet) { + if (conf.get(name) != null) { + conf.set(name, ""); + } + } + } + + public static boolean isLoadMetastoreConfig() { + return loadMetastoreConfig; + } + + public static void setLoadMetastoreConfig(boolean loadMetastoreConfig) { + HiveConf.loadMetastoreConfig = loadMetastoreConfig; + } + + public static boolean isLoadHiveServer2Config() { + return loadHiveServer2Config; + } + + public static void setLoadHiveServer2Config(boolean loadHiveServer2Config) { + HiveConf.loadHiveServer2Config = loadHiveServer2Config; + } + + public static String getNonMrEngines() { + String result = ""; + for (String s : ConfVars.HIVE_EXECUTION_ENGINE.getValidStringValues()) { + if ("mr".equals(s)) continue; + if (!result.isEmpty()) { + result += ", "; + } + result += s; + } + return result; + } + + public static String generateMrDeprecationWarning() { + return "Hive-on-MR is deprecated in Hive 2 and may not be available in the future versions. " + + "Consider using a different execution engine (i.e. " + HiveConf.getNonMrEngines() + + ") or using Hive 1.X releases."; + } +} diff --git common/src/java/org/apache/hive/common/util/HashCodeUtil.java common/src/java/org/apache/hive/common/util/HashCodeUtil.java index fa30273..805ccad 100644 --- common/src/java/org/apache/hive/common/util/HashCodeUtil.java +++ common/src/java/org/apache/hive/common/util/HashCodeUtil.java @@ -69,12 +69,10 @@ public static int calculateBytesHashCode(byte[] keyBytes, int keyStart, int keyL } public static void calculateBytesArrayHashCodes(byte[][] bytesArrays, - int[] starts, int[] lengths, int[] valueSelected, int[] hashCodes, final int count) { + int[] starts, int[] lengths, int[] hashCodes, final int count) { for (int i = 0; i < count; i++) { - int batchIndex = valueSelected[i]; - hashCodes[i] = murmurHash(bytesArrays[batchIndex], starts[batchIndex], - lengths[batchIndex]); + hashCodes[i] = murmurHash(bytesArrays[i], starts[i], lengths[i]); } } diff --git ql/pom.xml ql/pom.xml index ee1d46c..18a61cc 100644 --- ql/pom.xml +++ ql/pom.xml @@ -695,6 +695,7 @@ classpath="${compile.classpath}"/> + diff --git ql/pom.xml.orig ql/pom.xml.orig new file mode 100644 index 0000000..ee1d46c --- /dev/null +++ ql/pom.xml.orig @@ -0,0 +1,838 @@ + + + + 4.0.0 + + org.apache.hive + hive + 2.1.0-SNAPSHOT + ../pom.xml + + + hive-exec + jar + Hive Query Language + + + .. + + + + + + + + org.apache.hive + hive-ant + ${project.version} + + + org.apache.hive + hive-common + ${project.version} + + + org.eclipse.jetty.aggregate + jetty-all + + + + + org.apache.hive + hive-metastore + ${project.version} + + + org.apache.hive + hive-serde + ${project.version} + + + org.apache.hive + hive-llap-client + ${project.version} + + + org.apache.hive + hive-shims + ${project.version} + + + org.apache.hive + spark-client + ${project.version} + + + + com.esotericsoftware + kryo-shaded + ${kryo.version} + + + org.apache.parquet + parquet-hadoop-bundle + ${parquet.version} + + + commons-codec + commons-codec + ${commons-codec.version} + + + commons-httpclient + commons-httpclient + ${commons-httpclient.version} + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + + + commons-io + commons-io + ${commons-io.version} + + + org.apache.commons + commons-lang3 + ${commons-lang3.version} + + + commons-lang + commons-lang + ${commons-lang.version} + + + javolution + javolution + ${javolution.version} + + + org.apache.logging.log4j + log4j-1.2-api + ${log4j2.version} + + + org.apache.logging.log4j + log4j-slf4j-impl + ${log4j2.version} + + + org.antlr + antlr-runtime + ${antlr.version} + + + org.antlr + ST4 + ${ST4.version} + + + org.apache.avro + avro + ${avro.version} + + + org.apache.avro + avro-mapred + hadoop2 + ${avro.version} + + + org.mortbay.jetty + servlet-api + + + + + org.apache.ant + ant + ${ant.version} + + + org.apache.commons + commons-compress + ${commons-compress.version} + + + org.apache.thrift + libfb303 + ${libfb303.version} + + + org.apache.hadoop + hadoop-common + ${hadoop.version} + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + javax.servlet + servlet-api + + + true + + + org.apache.hadoop + hadoop-archives + ${hadoop.version} + true + + + org.apache.hadoop + hadoop-mapreduce-client-core + ${hadoop.version} + true + + + org.apache.hadoop + hadoop-mapreduce-client-common + ${hadoop.version} + true + test + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + + + org.apache.hadoop + hadoop-hdfs + ${hadoop.version} + + + javax.servlet + servlet-api + + + true + + + org.apache.hadoop + hadoop-yarn-api + ${hadoop.version} + true + + + org.apache.hadoop + hadoop-yarn-common + ${hadoop.version} + + + javax.servlet + servlet-api + + + true + + + org.apache.hadoop + hadoop-yarn-client + ${hadoop.version} + true + + + + org.apache.ivy + ivy + ${ivy.version} + + + org.apache.thrift + libthrift + ${libthrift.version} + + + org.apache.zookeeper + zookeeper + ${zookeeper.version} + + + org.apache.curator + curator-framework + ${curator.version} + + + org.apache.curator + apache-curator + ${curator.version} + pom + + + org.apache.curator + curator-test + ${curator.version} + test + + + org.codehaus.groovy + groovy-all + ${groovy.version} + + + org.codehaus.jackson + jackson-core-asl + ${jackson.version} + + + org.jodd + jodd-core + ${jodd.version} + + + org.codehaus.jackson + jackson-mapper-asl + ${jackson.version} + + + org.datanucleus + datanucleus-core + ${datanucleus-core.version} + + + org.apache.calcite + calcite-core + ${calcite.version} + + + + org.hsqldb + hsqldb + + + com.fasterxml.jackson.core + jackson-databind + + + com.fasterxml.jackson.core + jackson-annotations + + + com.fasterxml.jackson.core + jackson-core + + + + + org.apache.calcite + calcite-avatica + ${calcite.version} + + + + org.hsqldb + hsqldb + + + com.fasterxml.jackson.core + jackson-databind + + + com.fasterxml.jackson.core + jackson-annotations + + + com.fasterxml.jackson.core + jackson-core + + + + + com.google.guava + guava + ${guava.version} + + + com.google.protobuf + protobuf-java + ${protobuf.version} + + + com.googlecode.javaewah + JavaEWAH + ${javaewah.version} + + + com.google.code.gson + gson + ${gson.version} + + + org.iq80.snappy + snappy + ${snappy.version} + + + org.json + json + ${json.version} + + + stax + stax-api + ${stax.version} + + + net.sf.opencsv + opencsv + ${opencsv.version} + + + + + org.apache.parquet + parquet-column + tests + test + + + junit + junit + ${junit.version} + test + + + org.mockito + mockito-all + ${mockito-all.version} + test + + + jline + jline + ${jline.version} + + + org.apache.tez + tez-api + ${tez.version} + true + + + org.apache.hadoop + hadoop-common + + + org.apache.hadoop + hadoop-mapreduce-client-core + + + org.apache.hadoop + hadoop-mapreduce-client-jobclient + + + org.apache.hadoop + hadoop-mapreduce-client-common + + + org.apache.hadoop + hadoop-hdfs + + + org.apache.hadoop + hadoop-yarn-client + + + javax.servlet + servlet-api + + + + + org.apache.tez + tez-runtime-library + ${tez.version} + true + + + org.apache.hadoop + hadoop-common + + + org.apache.hadoop + hadoop-mapreduce-client-core + + + org.apache.hadoop + hadoop-mapreduce-client-jobclient + + + org.apache.hadoop + hadoop-mapreduce-client-common + + + org.apache.hadoop + hadoop-hdfs + + + org.apache.hadoop + hadoop-yarn-client + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + + + org.apache.tez + tez-runtime-internals + ${tez.version} + true + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + org.apache.hadoop + hadoop-common + + + org.apache.hadoop + hadoop-mapreduce-client-core + + + org.apache.hadoop + hadoop-mapreduce-client-jobclient + + + org.apache.hadoop + hadoop-mapreduce-client-common + + + org.apache.hadoop + hadoop-hdfs + + + org.apache.hadoop + hadoop-yarn-client + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + + + org.apache.tez + tez-mapreduce + ${tez.version} + true + + + org.apache.hadoop + hadoop-common + + + org.apache.hadoop + hadoop-mapreduce-client-core + + + org.apache.hadoop + hadoop-mapreduce-client-jobclient + + + org.apache.hadoop + hadoop-mapreduce-client-common + + + org.apache.hadoop + hadoop-hdfs + + + org.apache.hadoop + hadoop-yarn-client + + + + + org.apache.spark + spark-core_${scala.binary.version} + ${spark.version} + true + + + com.esotericsoftware.kryo + kryo + + + org.slf4j + slf4j-log4j12 + + + commmons-logging + commons-logging + + + + + com.sun.jersey + jersey-servlet + test + + + + + + protobuf + + + + org.apache.maven.plugins + maven-antrun-plugin + + + generate-protobuf-sources + generate-sources + + + + + Building ORC Protobuf + + + + + + + + + + run + + + + + + + + + + + ${basedir}/src/java + ${basedir}/src/test + + + + org.antlr + antlr3-maven-plugin + + + + antlr + + + + + ${basedir}/src/java + + **/HiveLexer.g + **/HiveParser.g + + + + + org.apache.maven.plugins + maven-antrun-plugin + + + generate-sources + generate-sources + + + + + + + + + + + + run + + + + + + org.apache.maven.plugins + maven-jar-plugin + + + + test-jar + + + + + core-jar + package + + jar + + + core + + + + + + org.apache.maven.plugins + maven-shade-plugin + + + build-exec-bundle + package + + shade + + + + + + + org.apache.hive:hive-common + org.apache.hive:hive-exec + org.apache.hive:hive-serde + org.apache.hive:hive-llap-client + org.apache.hive:hive-metastore + com.esotericsoftware:kryo-shaded + com.esotericsoftware:minlog + org.objenesis:objenesis + org.apache.parquet:parquet-hadoop-bundle + org.apache.thrift:libthrift + org.apache.thrift:libfb303 + javax.jdo:jdo-api + commons-lang:commons-lang + org.apache.commons:commons-lang3 + org.jodd:jodd-core + org.json:json + org.apache.avro:avro + org.apache.avro:avro-mapred + org.apache.hive.shims:hive-shims-0.23 + org.apache.hive.shims:hive-shims-0.23 + org.apache.hive.shims:hive-shims-common + org.apache.hive:hive-storage-api + com.googlecode.javaewah:JavaEWAH + javolution:javolution + com.google.protobuf:protobuf-java + org.iq80.snappy:snappy + org.codehaus.jackson:jackson-core-asl + org.codehaus.jackson:jackson-mapper-asl + com.google.guava:guava + net.sf.opencsv:opencsv + org.apache.hive:spark-client + org.apache.hive:hive-storage-api + joda-time:joda-time + + + + + com.esotericsoftware + org.apache.hive.com.esotericsoftware + + + org.objenesis + org.apache.hive.org.objenesis + + + + + + + + org.codehaus.mojo + build-helper-maven-plugin + + + add-source + generate-sources + + add-source + + + + src/gen/protobuf/gen-java + src/gen/thrift/gen-javabean + ${project.build.directory}/generated-sources/java + + + + + add-test-sources + generate-test-sources + + add-test-source + + + + ${project.build.directory}/generated-test-sources/java + + + + + + + + + diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrMap.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrMap.txt new file mode 100644 index 0000000..67bb8b7 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrMap.txt @@ -0,0 +1,42 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import java.util.ArrayList; +import java.util.Map; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr.BasicAggregation; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr.BatchVariation; + +/** + * + * This class contains a map from aggregation function set to aggregation class. + * + */ +public class { + + public static void fillMap(Map>> ) { + + ArrayList> classesArrayList; + + + + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNulls.txt new file mode 100644 index 0000000..859a518 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNulls.txt @@ -0,0 +1,139 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the NonSelected-NoNulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + = (() batch.cols[inputColumnNum]).vector; + } + + @Override + public void forgetColumnVectors() { + = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // NOT Selected + + if (duplicateCount == 1) { + + // Single Row. + + = [startIndex]; + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } else { + + // 2 or more in a series. + + // Race through the non-NULL values. + + int count = duplicateCount - 1; // First value is captured outside the loop. + int index = startIndex; + +// Variation BEGIN + = [index]; + +// Variation END + + ; + do { + index++; + = [index]; + +// Variation BEGIN + +// Variation END + + } while (--count > 0); + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNullsCount.txt new file mode 100644 index 0000000..cbe394e --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNoNullsCount.txt @@ -0,0 +1,113 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the {Count} NonSelected-NoNulls aggregates for + * a non-Key ColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + // Not used. + } + + @Override + public void forgetColumnVectors() { + // Not used. + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // NOT Selected + + if (duplicateCount == 1) { + + // Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } else { + + // 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNulls.txt new file mode 100644 index 0000000..faae5ab --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNulls.txt @@ -0,0 +1,165 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the NonSelected-Nulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + private boolean[] isNull; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + ColVector = () batch.cols[inputColumnNum]; + = ColVector.vector; + isNull = ColVector.isNull; + } + + @Override + public void forgetColumnVectors() { + = null; + isNull = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // NOT Selected + + if (duplicateCount == 1) { + + if (!isNull[startIndex]) { + + // Single Row. + + = [startIndex]; + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + } else { + + // 2 or more in a series. + + // Individual NULL checking. + + int count = duplicateCount; + int index = startIndex; + + // Outer loop looks for first non-NULL value. + do { + if (!isNull[index]) { + + int nonNullCount = 1; + +// Variation BEGIN + = [index]; + +// Variation END + + // Inner loop looks for more non-NULL values. + ; + while (--count > 0) { + index++; + if (!isNull[index]) { + nonNullCount++; + = [index]; + +// Variation BEGIN + +// Variation END + + } + } + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + index++; + } while (--count > 0); + } + + // We found no values this time -- return the old flag. + return hasValues; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNullsCount.txt new file mode 100644 index 0000000..881a033 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrNonSelectedNullsCount.txt @@ -0,0 +1,146 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the NonSelected-Nulls aggregates for + * a non-Key ColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + private boolean[] isNull; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + isNull = batch.cols[inputColumnNum].isNull; + } + + @Override + public void forgetColumnVectors() { + isNull = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // NOT Selected + + if (duplicateCount == 1) { + + if (!isNull[startIndex]) { + + // Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + } else { + + // 2 or more in a series. + + // Individual NULL checking. + + int count = duplicateCount; + int index = startIndex; + + // Outer loop looks for first non-NULL value. + do { + if (!isNull[index]) { + + int nonNullCount = 1; + + // Inner loop looks for more non-NULL values. + while (--count > 0) { + index++; + if (!isNull[index]) { + nonNullCount++; + } + } + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + index++; + } while (--count > 0); + } + + // We found no values this time -- return the old flag. + return hasValues; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNulls.txt new file mode 100644 index 0000000..90ce9d3 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNulls.txt @@ -0,0 +1,120 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Repeating-NoNulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + = (() batch.cols[inputColumnNum]).vector; + } + + @Override + public void forgetColumnVectors() { + = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Repeating Column. + + = [0]; + + if (duplicateCount == 1) { + + // Repeating Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // Repeating 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNullsCount.txt new file mode 100644 index 0000000..4d0bb46 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNoNullsCount.txt @@ -0,0 +1,113 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Repeating-NoNulls aggregates for + * a non-Key ColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + } + + @Override + public void forgetColumnVectors() { + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Repeating Column. + + if (duplicateCount == 1) { + + // Repeating Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // Repeating 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNulls.txt new file mode 100644 index 0000000..e4528c6 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNulls.txt @@ -0,0 +1,129 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Repeating-Nulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + private boolean[] isNull; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + ColVector = () batch.cols[inputColumnNum]; + = ColVector.vector; + isNull = ColVector.isNull; + } + + @Override + public void forgetColumnVectors() { + = null; + isNull = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Repeating Column. + + if (!isNull[0]) { + + = [0]; + + if (duplicateCount == 1) { + + // Repeating Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // Repeating 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } + return true; + } + + return hasValues; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNullsCount.txt new file mode 100644 index 0000000..4a8096e --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrRepeatingNullsCount.txt @@ -0,0 +1,122 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Repeating-Nulls aggregates for + * a non-Key ColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + private boolean[] isNull; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + isNull = batch.cols[inputColumnNum].isNull; + } + + @Override + public void forgetColumnVectors() { + isNull = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Repeating Column. + + if (!isNull[0]) { + + if (duplicateCount == 1) { + + // Repeating Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // Repeating 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } + return true; + } + + return hasValues; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNulls.txt new file mode 100644 index 0000000..85c75a8 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNulls.txt @@ -0,0 +1,145 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Selected-NoNulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + private int[] selected; + + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + = (() batch.cols[inputColumnNum]).vector; + selected = batch.selected; + } + + @Override + public void forgetColumnVectors() { + = null; + selected = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Selected + + if (duplicateCount == 1) { + + // Single Row. + + = [selected[startIndex]]; + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // 2 or more in a series. + + // Race through the non-NULL values. + + int count = duplicateCount - 1; // First value is captured outside the loop. + int logical = startIndex; + +// Variation BEGIN + = [selected[logical]]; + +// Variation END + + ; + do { + logical++; + = [selected[logical]]; + +// Variation BEGIN + +// Variation END + + } while (--count > 0); + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNullsCount.txt new file mode 100644 index 0000000..65d171e --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNoNullsCount.txt @@ -0,0 +1,113 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Selected-NoNulls aggregates for + * a non-Key ColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + } + + @Override + public void forgetColumnVectors() { + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Selected + + if (duplicateCount == 1) { + + // Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + } else { + + // 2 or more in a series. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + } + + return true; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNulls.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNulls.txt new file mode 100644 index 0000000..efe1f94 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNulls.txt @@ -0,0 +1,172 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Selected-Nulls aggregates for + * a non-Key . + * + */ +public class extends VectorGroupByColAggr { + + private [] ; + private boolean[] isNull; + private int[] selected; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + ColVector = () batch.cols[inputColumnNum]; + = ColVector.vector; + isNull = ColVector.isNull; + selected = batch.selected; + } + + @Override + public void forgetColumnVectors() { + = null; + isNull = null; + selected = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Selected + + if (duplicateCount == 1) { + + int batchIndex = selected[startIndex]; + + if (!isNull[batchIndex]) { + + // Single Row. + + = [batchIndex]; + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + } else { + + // 2 or more in a series. + + // Individual NULL checking. + + int count = duplicateCount; + int logical = startIndex; + + // Outer loop looks for first non-NULL value. + do { + int index = selected[logical]; + if (!isNull[index]) { + + int nonNullCount = 1; + +// Variation BEGIN + = [index]; + +// Variation END + + // Inner loop looks for more non-NULL values. + ; + while (--count > 0) { + logical++; + index = selected[logical]; + if (!isNull[index]) { + nonNullCount++; + = [index]; + +// Variation BEGIN + +// Variation END + + } + } + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + logical++; + } while (--count > 0); + } + + // We found no values this time -- return the old flag. + return hasValues; + } +} \ No newline at end of file diff --git ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNullsCount.txt ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNullsCount.txt new file mode 100644 index 0000000..b446473 --- /dev/null +++ ql/src/gen/vectorization/GroupByAggregationTemplates/ColAggrSelectedNullsCount.txt @@ -0,0 +1,152 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; + +/** + * + * This class handles the Selected-Nulls aggregates for + * a non-Key LongColumnVector. + * + */ +public class extends VectorGroupByColAggr { + + private boolean[] isNull; + private int[] selected; + + + + public (int inputColumnNum) { + super(inputColumnNum); + } + + @Override + public void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess) { + + } + + @Override + public void setColumnVectors(VectorizedRowBatch batch) { + isNull = batch.cols[inputColumnNum].isNull; + selected = batch.selected; + } + + @Override + public void forgetColumnVectors() { + isNull = null; + selected = null; + } + + @Override + public int getAggrCount() { + return ; + } + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + @Override + public boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex) { + + // Selected + + if (duplicateCount == 1) { + + if (!isNull[selected[startIndex]]) { + + // Single Row. + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + } else { + + // 2 or more in a series. + + // Individual NULL checking. + + int count = duplicateCount; + int logical = startIndex; + + // Outer loop looks for first non-NULL value. + do { + int index = selected[logical]; + if (!isNull[index]) { + + int nonNullCount = 1; + + // Inner loop looks for more non-NULL values. + long value; + while (--count > 0) { + logical++; + index = selected[logical]; + if (!isNull[index]) { + nonNullCount++; + } + } + + // + if (!hasValues) { + +// Variation BEGIN + +// Variation END + + } else { + +// Variation BEGIN + +// Variation END + + } + + return true; + } + logical++; + } while (--count > 0); + } + + // We found no values this time -- return the old flag. + return hasValues; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/MapJoinOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/MapJoinOperator.java index cab0fc8..f7dbcd3 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/MapJoinOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/MapJoinOperator.java @@ -37,12 +37,11 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars; import org.apache.hadoop.hive.ql.HashTableLoaderFactory; import org.apache.hadoop.hive.ql.exec.mr.ExecMapperContext; -import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMap; +import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMapFactory; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer.HashPartition; import org.apache.hadoop.hive.ql.exec.persistence.KeyValueContainer; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer.KeyValueHelper; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinKey; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinObjectSerDeContext; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinRowContainer; @@ -51,6 +50,9 @@ import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainerSerDe; import org.apache.hadoop.hive.ql.exec.persistence.ObjectContainer; import org.apache.hadoop.hive.ql.exec.persistence.UnwrapRowContainer; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.spark.SparkUtilities; import org.apache.hadoop.hive.ql.io.HiveKey; import org.apache.hadoop.hive.ql.log.PerfLogger; @@ -91,7 +93,7 @@ protected transient ReusableGetAdaptor[] hashMapRowGetters; private UnwrapRowContainer[] unwrapContainer; - private transient Configuration hconf; + protected transient Configuration hconf; private transient boolean hybridMapJoinLeftover; // whether there's spilled data to be processed protected transient MapJoinBytesTableContainer[] spilledMapJoinTables; // used to hold restored // spilled small tables @@ -323,8 +325,8 @@ public void cleanUpInputFileChangedOp() throws HiveException { loadHashTable(getExecContext(), MapredContext.get()); } - protected JoinUtil.JoinResult setMapJoinKey( - ReusableGetAdaptor dest, Object row, byte alias) throws HiveException { + protected MapJoinResult setMapJoinKey( + ReusableGetAdaptor dest, Object row, byte alias) throws HiveException, IOException { return dest.setFromRow(row, joinKeys[alias], joinKeysObjectInspectors[alias]); } @@ -372,7 +374,7 @@ public void process(Object row, int tag) throws HiveException { boolean bigTableRowSpilled = false; for (byte pos = 0; pos < order.length; pos++) { if (pos != alias) { - JoinUtil.JoinResult joinResult; + MapJoinResult joinResult; ReusableGetAdaptor adaptor; if (firstSetKey == null) { adaptor = firstSetKey = hashMapRowGetters[pos]; @@ -383,7 +385,7 @@ public void process(Object row, int tag) throws HiveException { joinResult = adaptor.setFromOther(firstSetKey); } MapJoinRowContainer rowContainer = adaptor.getCurrentRows(); - if (joinResult != JoinUtil.JoinResult.MATCH) { + if (joinResult != MapJoinResult.MATCH) { assert (rowContainer == null || !rowContainer.hasRows()) : "Expecting an empty result set for no match"; } @@ -397,7 +399,7 @@ public void process(Object row, int tag) throws HiveException { // For Hybrid Grace Hash Join, during the 1st round processing, // we only keep the LEFT side if the row is not spilled if (!conf.isHybridHashJoin() || hybridMapJoinLeftover || - (joinResult != JoinUtil.JoinResult.SPILL && !bigTableRowSpilled)) { + (joinResult != MapJoinResult.SPILL && !bigTableRowSpilled)) { joinNeeded = true; storage[pos] = dummyObjVectors[pos]; } else { @@ -415,7 +417,7 @@ public void process(Object row, int tag) throws HiveException { // When the JoinResult is SPILL, it means the corresponding small table row may have been // spilled to disk (at least the partition that holds this row is on disk). So we need to // postpone the join processing for this pair by also spilling this big table row. - if (joinResult == JoinUtil.JoinResult.SPILL && + if (joinResult == MapJoinResult.SPILL && !bigTableRowSpilled) { // For n-way join, only spill big table rows once spillBigTableRow(mapJoinTables[pos], row); bigTableRowSpilled = true; @@ -501,8 +503,8 @@ public void closeOp(boolean abort) throws HiveException { if (!hashPartitions[i].isHashMapOnDisk()) { hybridHtContainer.setTotalInMemRowCount( hybridHtContainer.getTotalInMemRowCount() - - hashPartitions[i].getHashMapFromMemory().getNumValues()); - hashPartitions[i].getHashMapFromMemory().clear(); + hashPartitions[i].getHashTableFromMemory().getNumValues()); + hashPartitions[i].getHashTableFromMemory().clear(); } } assert hybridHtContainer.getTotalInMemRowCount() == 0; @@ -608,7 +610,7 @@ protected void reloadHashTable(byte pos, int partitionId) // as the initialCapacity which cannot be 0, we provide a reasonable // positive number here } - BytesBytesMultiHashMap restoredHashMap = partition.getHashMapFromDisk(rowCount); + MapJoinHashTable restoredHashMap = partition.getHashMapFromDisk(rowCount); rowCount += restoredHashMap.getNumValues(); LOG.info("Hybrid Grace Hash Join: Deserializing spilled hash partition..."); LOG.info("Hybrid Grace Hash Join: Number of rows in hashmap: " + rowCount); @@ -621,20 +623,21 @@ protected void reloadHashTable(byte pos, int partitionId) " will be greater than memory limit. Recursive spilling is currently not supported"); } - KeyValueHelper writeHelper = container.getWriteHelper(); + KeyValuePut writeHelper = container.getKeyValuePutHelper(); while (kvContainer.hasNext()) { ObjectPair pair = kvContainer.next(); Writable key = pair.getFirst(); Writable val = pair.getSecond(); writeHelper.setKeyValue(key, val); - restoredHashMap.put(writeHelper, -1); + restoredHashMap.put(writeHelper); } container.setTotalInMemRowCount(container.getTotalInMemRowCount() + restoredHashMap.getNumValues()); kvContainer.clear(); - spilledMapJoinTables[pos] = new MapJoinBytesTableContainer(restoredHashMap); + spilledMapJoinTables[pos] = new MapJoinBytesTableContainer( + new BytesBytesMultiHashMapFactory(), restoredHashMap); spilledMapJoinTables[pos].setInternalValueOi(container.getInternalValueOi()); spilledMapJoinTables[pos].setSortableSortOrders(container.getSortableSortOrders()); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/ReduceSinkOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/ReduceSinkOperator.java index ef5ee95..c0c079b 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/ReduceSinkOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/ReduceSinkOperator.java @@ -31,6 +31,7 @@ import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.conf.HiveConf; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.io.AcidUtils; import org.apache.hadoop.hive.ql.io.HiveKey; import org.apache.hadoop.hive.ql.metadata.HiveException; @@ -535,6 +536,13 @@ protected void collect(BytesWritable keyWritable, Writable valueWritable) throws LOG.info(toString() + ": records written - " + numRows); } } + + // BytesWritable valueBytesWritable = (BytesWritable) valueWritable; + // LOG.info("ReduceSinkOperator " + getOperatorId() + " collect keyWritable " + keyWritable.getLength() + " " + + // VectorizedBatchUtil.displayBytes(keyWritable.getBytes(), 0, keyWritable.getLength()) + + // " valueWritable " + valueBytesWritable.getLength() + + // VectorizedBatchUtil.displayBytes(valueBytesWritable.getBytes(), 0, valueBytesWritable.getLength())); + out.collect(keyWritable, valueWritable); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMap.java index 51acae0..6a7c0e8 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMap.java @@ -18,6 +18,7 @@ package org.apache.hadoop.hive.ql.exec.persistence; +import java.io.IOException; import java.util.ArrayList; import java.util.List; import java.util.Map; @@ -27,9 +28,14 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.hadoop.hive.ql.debug.Utils; -import org.apache.hadoop.hive.serde2.ByteStream.RandomAccessOutput; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResultImpl; import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.hive.serde2.WriteBuffers; +import org.apache.hive.common.util.HashCodeUtil; import com.google.common.annotations.VisibleForTesting; @@ -45,7 +51,7 @@ * Initially inspired by HPPC LongLongOpenHashMap; however, the code is almost completely reworked * and there's very little in common left save for quadratic probing (and that with some changes). */ -public final class BytesBytesMultiHashMap { +public final class BytesBytesMultiHashMap implements MapJoinHashTable { public static final Logger LOG = LoggerFactory.getLogger(BytesBytesMultiHashMap.class); /* @@ -194,7 +200,7 @@ public BytesBytesMultiHashMap(int initialCapacity, * * This object can read through the 0, 1, or more values found for the key. */ - public static class Result { + public static class Result extends MapJoinHashTableResultImpl implements MapJoinHashMapResult { // Whether there are more than 0 rows. private boolean hasRows; @@ -221,6 +227,9 @@ public BytesBytesMultiHashMap(int initialCapacity, // A reference to the current row. private WriteBuffers.ByteSegmentRef byteSegmentRef; + // The associated alias filter value. + private byte aliasFilter; + public Result() { hasRows = false; byteSegmentRef = new WriteBuffers.ByteSegmentRef(); @@ -262,13 +271,16 @@ public boolean isSingleRow() { * The offset of just after the key length in the list record. Or, 0 when single row. */ public void set(BytesBytesMultiHashMap hashMap, long firstOffset, boolean hasList, - long offsetAfterListRecordKeyLen) { + long offsetAfterListRecordKeyLen, byte aliasFilter) { + + this.mapJoinResult = MapJoinResult.MATCH; this.hashMap = hashMap; this.firstOffset = firstOffset; this.hasList = hasList; this.offsetAfterListRecordKeyLen = offsetAfterListRecordKeyLen; + this.aliasFilter = aliasFilter; // Position at first row. readIndex = 0; @@ -412,22 +424,26 @@ public void forget() { readIndex = 0; nextTailOffset = -1; } - } - /** The source of keys and values to put into hashtable; avoids byte copying. */ - public static interface KvSource { - /** Write key into output. */ - public void writeKey(RandomAccessOutput dest) throws SerDeException; + @Override + public boolean isCappedCountAvailable() { + return false; + } - /** Write value into output. */ - public void writeValue(RandomAccessOutput dest) throws SerDeException; + @Override + public int cappedCount() { + return 0; + } - /** - * Provide updated value for state byte for a key. - * @param previousValue Previous value; null if this is the first call per key. - * @return The updated value. - */ - public byte updateStateByte(Byte previousValue); + @Override + public boolean isAliasFilterAvailable() { + return true; + } + + @Override + public byte aliasFilter() { + return aliasFilter; + } } /** @@ -435,11 +451,15 @@ public void forget() { * @param kv Keyvalue writer. Each method will be called at most once. */ private static final byte[] FOUR_ZEROES = new byte[] { 0, 0, 0, 0 }; - public void put(KvSource kv, int keyHashCode) throws SerDeException { + + @Override + public void put(KeyValuePut keyValuePut) throws SerDeException { if (resizeThreshold <= keysAssigned) { expandAndRehash(); } + KeyValuePutWriter keyValuePutWriter = (KeyValuePutWriter) keyValuePut; + // Reserve 4 bytes for the hash (don't just reserve, there may be junk there) writeBuffers.write(FOUR_ZEROES); @@ -447,9 +467,11 @@ public void put(KvSource kv, int keyHashCode) throws SerDeException { // become part of the record; otherwise, we will just write over it later. long keyOffset = writeBuffers.getWritePoint(); - kv.writeKey(writeBuffers); + keyValuePutWriter.writeKey(writeBuffers); int keyLength = (int)(writeBuffers.getWritePoint() - keyOffset); - int hashCode = (keyHashCode == -1) ? writeBuffers.hashCode(keyOffset, keyLength) : keyHashCode; + int hashCode = (keyValuePut.hasHashCode()) ? + keyValuePut.getKeyHashCode() : + writeBuffers.hashCode(keyOffset, keyLength); int slot = findKeySlotToWrite(keyOffset, keyLength, hashCode); // LOG.info("Write hash code is " + Integer.toBinaryString(hashCode) + " - " + slot); @@ -457,18 +479,18 @@ public void put(KvSource kv, int keyHashCode) throws SerDeException { long ref = refs[slot]; if (ref == 0) { // This is a new key, keep writing the first record. - long tailOffset = writeFirstValueRecord(kv, keyOffset, keyLength, hashCode); - byte stateByte = kv.updateStateByte(null); + long tailOffset = writeFirstValueRecord(keyValuePutWriter, keyOffset, keyLength, hashCode); + byte stateByte = keyValuePutWriter.updateStateByte(null); refs[slot] = Ref.makeFirstRef(tailOffset, stateByte, hashCode, startingHashBitCount); ++keysAssigned; } else { // This is not a new key; we'll overwrite the key and hash bytes - not needed anymore. writeBuffers.setWritePoint(keyOffset - 4); long lrPtrOffset = createOrGetListRecord(ref); - long tailOffset = writeValueAndLength(kv); + long tailOffset = writeValueAndLength(keyValuePutWriter); addRecordToList(lrPtrOffset, tailOffset); byte oldStateByte = Ref.getStateByte(ref); - byte stateByte = kv.updateStateByte(oldStateByte); + byte stateByte = keyValuePutWriter.updateStateByte(oldStateByte); if (oldStateByte != stateByte) { ref = Ref.setStateByte(ref, stateByte); } @@ -486,18 +508,22 @@ public void put(KvSource kv, int keyHashCode) throws SerDeException { * @param key Key buffer. * @param offset the offset to the key in the buffer * @param hashMapResult The object to fill in that can read the values. - * @return The state byte. */ - public byte getValueResult(byte[] key, int offset, int length, Result hashMapResult) { + @Override + public void hashMapLookup(byte[] keyBytes, int keyStart, int keyLength, + int hashCode, MapJoinHashMapResult hashMapResult) { hashMapResult.forget(); - WriteBuffers.Position readPos = hashMapResult.getReadPos(); + Result internalHashMapResult = (Result) hashMapResult; + + WriteBuffers.Position readPos = internalHashMapResult.getReadPos(); // First, find first record for the key. - long ref = findKeyRefToRead(key, offset, length, readPos); + long ref = findKeyRefToRead(keyBytes, keyStart, keyLength, readPos); if (ref == 0) { - return 0; + hashMapResult.setNoMatch(); + return; } boolean hasList = Ref.hasList(ref); @@ -505,9 +531,8 @@ public byte getValueResult(byte[] key, int offset, int length, Result hashMapRes // This relies on findKeyRefToRead doing key equality check and leaving read ptr where needed. long offsetAfterListRecordKeyLen = hasList ? writeBuffers.getReadPoint(readPos) : 0; - hashMapResult.set(this, Ref.getOffset(ref), hasList, offsetAfterListRecordKeyLen); - - return Ref.getStateByte(ref); + internalHashMapResult.set(this, Ref.getOffset(ref), hasList, offsetAfterListRecordKeyLen, + Ref.getStateByte(ref)); } /** @@ -523,6 +548,7 @@ public void populateValue(WriteBuffers.ByteSegmentRef valueRef) { * Number of keys in the hashmap * @return number of keys */ + @Override public int size() { return keysAssigned; } @@ -532,6 +558,7 @@ public int size() { * This is equal to or bigger than number of keys, since some values may share the same key * @return number of values */ + @Override public int getNumValues() { return numValues; } @@ -542,6 +569,7 @@ public int getNumValues() { * Others include instance fields: 100 * @return number of bytes */ + @Override public long memorySize() { return writeBuffers.size() + refs.length * 8 + 100; } @@ -558,6 +586,7 @@ public void clear() { this.numValues = 0; } + @Override public void expandAndRehashToTarget(int estimateNewRowCount) { int oldRefsCount = refs.length; int newRefsCount = oldRefsCount + estimateNewRowCount; @@ -741,7 +770,7 @@ private void expandAndRehash() { long capacity = refs.length << 1; expandAndRehashImpl(capacity); } - + private void expandAndRehashImpl(long capacity) { long expandTime = System.currentTimeMillis(); final long[] oldRefs = refs; @@ -834,9 +863,9 @@ private void addRecordToList(long lrPtrOffset, long tailOffset) { * @return The offset of the new record. */ private long writeFirstValueRecord( - KvSource kv, long keyOffset, int keyLength, int hashCode) throws SerDeException { + KeyValuePutWriter keyValuePutWriter, long keyOffset, int keyLength, int hashCode) throws SerDeException { long valueOffset = writeBuffers.getWritePoint(); - kv.writeValue(writeBuffers); + keyValuePutWriter.writeValue(writeBuffers); long tailOffset = writeBuffers.getWritePoint(); int valueLength = (int)(tailOffset - valueOffset); // LOG.info("Writing value at " + valueOffset + " length " + valueLength); @@ -863,9 +892,9 @@ private long writeFirstValueRecord( * @param kv Key-value writer. * @return The offset of the new record. */ - private long writeValueAndLength(KvSource kv) throws SerDeException { + private long writeValueAndLength(KeyValuePutWriter keyValuePutWriter) throws SerDeException { long valueOffset = writeBuffers.getWritePoint(); - kv.writeValue(writeBuffers); + keyValuePutWriter.writeValue(writeBuffers); long tailOffset = writeBuffers.getWritePoint(); writeBuffers.writeVLong(tailOffset - valueOffset); // LOG.info("Writing value at " + valueOffset + " length " + (tailOffset - valueOffset)); @@ -901,7 +930,8 @@ public void debugDumpTable() { dump.append(Utils.toStringBinary(key, 0, key.length)).append(" ref [").append(dumpRef(ref)) .append("]: "); Result hashMapResult = new Result(); - getValueResult(key, 0, key.length, hashMapResult); + int hashCode = HashCodeUtil.calculateBytesHashCode(key, 0, key.length); + hashMapLookup(key, 0, key.length, hashCode, hashMapResult); List results = new ArrayList(); WriteBuffers.ByteSegmentRef byteSegmentRef = hashMapResult.first(); while (byteSegmentRef != null) { @@ -998,6 +1028,7 @@ private static String dumpRef(long ref) { + " h=" + Long.toBinaryString(Ref.getHashBits(ref)); } + @Override public void debugDumpMetrics() { LOG.info("Map metrics: keys allocated " + this.refs.length +", keys assigned " + keysAssigned + ", write conflict " + metricPutConflict + ", write max dist " + largestNumberOfSteps @@ -1024,4 +1055,51 @@ private void debugDumpKeyProbe(long keyOffset, int keyLength, int hashCode, int } LOG.info(sb.toString()); } + + // New methods for Native Vector Map Join not implemented here. + @Override + public boolean useMinMax() { + return false; + } + + @Override + public long min() { + throw new RuntimeException("Not supported"); + } + + @Override + public long max() { + throw new RuntimeException("Not supported"); + } + + @Override + public void hashMapLookup(long key, int hashCode, + MapJoinHashMapResult hashMapResult) throws IOException { + throw new RuntimeException("Not supported"); + } + + @Override + public void hashMultiSetContains(byte[] keyBytes, int keyStart, + int keyLength, int hashCode, MapJoinHashMultiSetResult hashMultiSetResult) + throws IOException { + throw new RuntimeException("Not supported"); + } + + @Override + public void hashMultiSetContains(long key, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) throws IOException { + throw new RuntimeException("Not supported"); + } + + @Override + public void hashSetContains(byte[] keyBytes, int keyStart, int keyLength, + int hashCode, MapJoinHashSetResult hashSetResult) throws IOException { + throw new RuntimeException("Not supported"); + } + + @Override + public void hashSetContains(long key, int hashCode, + MapJoinHashSetResult hashSetResult) throws IOException { + throw new RuntimeException("Not supported"); + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMapFactory.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMapFactory.java new file mode 100644 index 0000000..6d28a4e --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/BytesBytesMultiHashMapFactory.java @@ -0,0 +1,94 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence; + +import java.util.Arrays; +import java.util.List; + +import org.apache.commons.logging.Log; +import org.apache.commons.logging.LogFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public class BytesBytesMultiHashMapFactory implements MapJoinHashTableFactory { + + public static final Log LOG = LogFactory.getLog(BytesBytesMultiHashMapFactory.class); + + @Override + public MapJoinHashTable createHashTable(int initialCapacity, float loadFactor, + int writeBuffersSize, int maxProbeSize) { + return new BytesBytesMultiHashMap(initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + } + + /* + * @return A new hash map result implementation specific object. + * + * The object can be used to access the values when there is a match, or + * access spill information when the partition with the key is currently spilled. + */ + @Override + public MapJoinHashMapResult createHashMapResult() { + return (MapJoinHashMapResult) new BytesBytesMultiHashMap.Result(); + } + + /* + * @return A new hash multi-set result implementation specific object. + * + * The object can be used to access the *count* of values when the key is contained in the + * multi-set, or access spill information when the partition with the key is currently spilled. + */ + @Override + public MapJoinHashMultiSetResult createHashMultiSetResult() { + throw new RuntimeException("Not supported"); + } + + /* + * @return A new hash set result implementation specific object. + * + * The object can be used to access access spill information when the partition with the key + * is currently spilled. + */ + @Override + public MapJoinHashSetResult createHashSetResult() { + throw new RuntimeException("Not supported"); + } + + @Override + public boolean keyValuePutHelperIsExternal() { + return false; + } + + @Override + public KeyValuePut createKeyValuePutHelper() { + return null; + } + + @Override + public boolean useMinMax() { + return false; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HashMapWrapper.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HashMapWrapper.java index 2ca5c00..1906634 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HashMapWrapper.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HashMapWrapper.java @@ -32,6 +32,9 @@ import org.apache.hadoop.hive.conf.HiveConf; import org.apache.hadoop.hive.ql.exec.ExprNodeEvaluator; import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapper; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapperBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriter; @@ -154,7 +157,7 @@ public GetAdaptor(MapJoinKey key) { } @Override - public JoinUtil.JoinResult setFromVector(VectorHashKeyWrapper kw, + public MapJoinResult setFromVector(VectorHashKeyWrapper kw, VectorExpressionWriter[] keyOutputWriters, VectorHashKeyWrapperBatch keyWrapperBatch) throws HiveException { if (currentKey == null) { @@ -171,15 +174,15 @@ public GetAdaptor(MapJoinKey key) { isFirstKey = false; this.currentValue = mHash.get(key); if (this.currentValue == null) { - return JoinUtil.JoinResult.NOMATCH; + return MapJoinResult.NO_MATCH; } else { - return JoinUtil.JoinResult.MATCH; + return MapJoinResult.MATCH; } } @Override - public JoinUtil.JoinResult setFromRow(Object row, List fields, + public MapJoinResult setFromRow(Object row, List fields, List ois) throws HiveException { if (currentKey == null) { currentKey = new Object[fields.size()]; @@ -191,25 +194,25 @@ public GetAdaptor(MapJoinKey key) { isFirstKey = false; this.currentValue = mHash.get(key); if (this.currentValue == null) { - return JoinUtil.JoinResult.NOMATCH; + return MapJoinResult.NO_MATCH; } else { - return JoinUtil.JoinResult.MATCH; + return MapJoinResult.MATCH; } } @Override - public JoinUtil.JoinResult setFromOther(ReusableGetAdaptor other) { + public MapJoinResult setFromOther(ReusableGetAdaptor other) { assert other instanceof GetAdaptor; GetAdaptor other2 = (GetAdaptor)other; this.key = other2.key; this.isFirstKey = other2.isFirstKey; this.currentValue = mHash.get(key); if (this.currentValue == null) { - return JoinUtil.JoinResult.NOMATCH; + return MapJoinResult.NO_MATCH; } else { - return JoinUtil.JoinResult.MATCH; + return MapJoinResult.MATCH; } } @@ -248,4 +251,14 @@ public void dumpMetrics() { public boolean hasSpill() { return false; } + + @Override + public MapJoinHashTableFind getMapJoinHashTableFind() { + throw new RuntimeException("Not supported"); + } + + @Override + public MapJoinHashTableFactory getMapJoinHashTableFactory() { + throw new RuntimeException("Not supported"); + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HybridHashTableContainer.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HybridHashTableContainer.java index a0c9b98..f2842f0 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HybridHashTableContainer.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/HybridHashTableContainer.java @@ -35,10 +35,15 @@ import org.apache.hive.common.util.HashCodeUtil; import org.apache.hadoop.hive.conf.HiveConf; import org.apache.hadoop.hive.ql.exec.ExprNodeEvaluator; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; import org.apache.hadoop.hive.ql.exec.Utilities; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer.KeyValueHelper; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapper; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapperBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriter; @@ -72,9 +77,18 @@ * be restored and processed one by one. */ public class HybridHashTableContainer - implements MapJoinTableContainer, MapJoinTableContainerDirectAccess { + implements MapJoinTableContainer, MapJoinHashTableFind { private static final Logger LOG = LoggerFactory.getLogger(HybridHashTableContainer.class); + private final MapJoinHashTableFactory mapJoinHashTableFactory; + // Factory for creating hash tables. + + private final boolean useMinMax; + // Whether we should maintain min/max for + // small table for optimizing lookup. + private long longMin; + private long longMax; + private final HashPartition[] hashPartitions; // an array of partitions holding the triplets private int totalInMemRowCount = 0; // total number of small table rows in memory private long memoryThreshold; // the max memory limit that can be allocated @@ -91,8 +105,8 @@ /** The OI used to deserialize values. We never deserialize keys. */ private LazyBinaryStructObjectInspector internalValueOi; private boolean[] sortableSortOrders; - private MapJoinBytesTableContainer.KeyValueHelper writeHelper; - private MapJoinBytesTableContainer.DirectKeyValueWriter directWriteHelper; + private KeyValuePut keyValuePut; + /* * this is not a real bloom filter, but is a cheap version of the 1-memory * access bloom filters @@ -113,7 +127,10 @@ * The triplet: hashmap (either in memory or on disk), small table container, big table container */ public static class HashPartition { - BytesBytesMultiHashMap hashMap; // In memory hashMap + MapJoinHashTableFactory mapJoinHashTableFactory; + // Hash table factor to use. + MapJoinHashTable hashTable; // In memory hashTable + Class hashTableClass; // Class of hashTable so we can bring it back from disk. KeyValueContainer sidefileKVContainer; // Stores small table key/value pairs ObjectContainer matchfileObjContainer; // Stores big table rows VectorMapJoinRowBytesContainer matchfileRowBytesContainer; @@ -121,7 +138,7 @@ Path hashMapLocalPath; // Local file system path for spilled hashMap boolean hashMapOnDisk; // Status of hashMap. true: on disk, false: in memory boolean hashMapSpilledOnCreation; // When there's no enough memory, cannot create hashMap - int initialCapacity; // Used to create an empty BytesBytesMultiHashMap + int initialCapacity; // Used to create an empty MapJoinHashTable float loadFactor; // Same as above int wbSize; // Same as above int rowsOnDisk; // How many rows saved to the on-disk hashmap (if on disk) @@ -129,12 +146,13 @@ /* It may happen that there's not enough memory to instantiate a hashmap for the partition. * In that case, we don't create the hashmap, but pretend the hashmap is directly "spilled". */ - public HashPartition(int initialCapacity, float loadFactor, int wbSize, long maxProbeSize, - boolean createHashMap) { + public HashPartition(int initialCapacity, float loadFactor, int wbSize, int maxProbeSize, + boolean createHashMap, MapJoinHashTableFactory mapJoinHashTableFactory) { + this.mapJoinHashTableFactory = mapJoinHashTableFactory; if (createHashMap) { // Probe space should be at least equal to the size of our designated wbSize maxProbeSize = Math.max(maxProbeSize, wbSize); - hashMap = new BytesBytesMultiHashMap(initialCapacity, loadFactor, wbSize, maxProbeSize); + hashTable = mapJoinHashTableFactory.createHashTable(initialCapacity, loadFactor, wbSize, maxProbeSize); } else { hashMapSpilledOnCreation = true; hashMapOnDisk = true; @@ -145,22 +163,24 @@ public HashPartition(int initialCapacity, float loadFactor, int wbSize, long max } /* Get the in memory hashmap */ - public BytesBytesMultiHashMap getHashMapFromMemory() { - return hashMap; + public MapJoinHashTable getHashTableFromMemory() { + return hashTable; } /* Restore the hashmap from disk by deserializing it. * Currently Kryo is used for this purpose. */ - public BytesBytesMultiHashMap getHashMapFromDisk(int rowCount) + public MapJoinHashTable getHashMapFromDisk(int rowCount) throws IOException, ClassNotFoundException { if (hashMapSpilledOnCreation) { - return new BytesBytesMultiHashMap(rowCount, loadFactor, wbSize, -1); + return mapJoinHashTableFactory.createHashTable(rowCount, loadFactor, wbSize, -1); } else { InputStream inputStream = Files.newInputStream(hashMapLocalPath); - com.esotericsoftware.kryo.io.Input input = new com.esotericsoftware.kryo.io.Input(inputStream); + com.esotericsoftware.kryo.io.Input input = + new com.esotericsoftware.kryo.io.Input(inputStream); Kryo kryo = Utilities.runtimeSerializationKryo.get(); - BytesBytesMultiHashMap restoredHashMap = kryo.readObject(input, BytesBytesMultiHashMap.class); + MapJoinHashTable restoredHashMap = + (MapJoinHashTable) kryo.readObject(input, hashTableClass); if (rowCount > 0) { restoredHashMap.expandAndRehashToTarget(rowCount); @@ -207,9 +227,9 @@ public boolean isHashMapOnDisk() { } public void clear() { - if (hashMap != null) { - hashMap.clear(); - hashMap = null; + if (hashTable != null) { + hashTable.clear(); + hashTable = null; } if (hashMapLocalPath != null) { @@ -244,13 +264,14 @@ public int size() { return rowsOnDisk + (sidefileKVContainer != null ? sidefileKVContainer.size() : 0); } else { // All rows should be in the in-memory hashmap - return hashMap.size(); + return hashTable.size(); } } } public HybridHashTableContainer(Configuration hconf, long keyCount, long memoryAvailable, - long estimatedTableSize, HybridHashTableConf nwayConf) + long estimatedTableSize, HybridHashTableConf nwayConf, + MapJoinHashTableFactory mapJoinHashTableFactory) throws SerDeException, IOException { this(HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEKEYCOUNTADJUSTMENT), HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLETHRESHOLD), @@ -260,14 +281,23 @@ public HybridHashTableContainer(Configuration hconf, long keyCount, long memoryA HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEWBSIZE), HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHYBRIDGRACEHASHJOINMINNUMPARTITIONS), HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEMAPJOINOPTIMIZEDTABLEPROBEPERCENT), - estimatedTableSize, keyCount, memoryAvailable, nwayConf); + estimatedTableSize, keyCount, memoryAvailable, nwayConf, mapJoinHashTableFactory); } private HybridHashTableContainer(float keyCountAdj, int threshold, float loadFactor, int memCheckFreq, int minWbSize, int maxWbSize, int minNumParts, float probePercent, - long estimatedTableSize, long keyCount, long memoryAvailable, HybridHashTableConf nwayConf) + long estimatedTableSize, long keyCount, long memoryAvailable, HybridHashTableConf nwayConf, + MapJoinHashTableFactory mapJoinHashTableFactory) throws SerDeException, IOException { - directWriteHelper = new MapJoinBytesTableContainer.DirectKeyValueWriter(); + + this.mapJoinHashTableFactory = mapJoinHashTableFactory; + useMinMax = mapJoinHashTableFactory.useMinMax(); + if (useMinMax) { + longMin = Long.MAX_VALUE; + longMax = Long.MIN_VALUE; + } else { + longMin = longMax = 0; + } int newKeyCount = HashMapWrapper.calculateTableSize( keyCountAdj, threshold, loadFactor, keyCount); @@ -336,21 +366,22 @@ private HybridHashTableContainer(float keyCountAdj, int threshold, float loadFac nwayConf.getLoadedContainerList().size() == 0) { // n-way join, first (biggest) small table if (i == 0) { // We unconditionally create a hashmap for the first hash partition hashPartitions[i] = new HashPartition(initialCapacity, loadFactor, writeBufferSize, - maxCapacity, true); + maxCapacity, true, mapJoinHashTableFactory); } else { // To check whether we have enough memory to allocate for another hash partition, // we need to get the size of the first hash partition to get an idea. hashPartitions[i] = new HashPartition(initialCapacity, loadFactor, writeBufferSize, - maxCapacity, memoryUsed + hashPartitions[0].hashMap.memorySize() < memoryThreshold); + maxCapacity, memoryUsed + hashPartitions[0].hashTable.memorySize() < memoryThreshold, + mapJoinHashTableFactory); } } else { // n-way join, all later small tables // For all later small tables, follow the same pattern of the previously loaded tables. if (this.nwayConf.doSpillOnCreation(i)) { hashPartitions[i] = new HashPartition(initialCapacity, loadFactor, writeBufferSize, - maxCapacity, false); + maxCapacity, false, mapJoinHashTableFactory); } else { hashPartitions[i] = new HashPartition(initialCapacity, loadFactor, writeBufferSize, - maxCapacity, true); + maxCapacity, true, mapJoinHashTableFactory); } } @@ -362,7 +393,7 @@ private HybridHashTableContainer(float keyCountAdj, int threshold, float loadFac this.nwayConf.setNextSpillPartition(i - 1); } } else { - memoryUsed += hashPartitions[i].hashMap.memorySize(); + memoryUsed += hashPartitions[i].hashTable.memorySize(); } } assert numPartitionsSpilledOnCreation != numPartitions : "All partitions are directly spilled!" + @@ -377,9 +408,8 @@ private HybridHashTableContainer(float keyCountAdj, int threshold, float loadFac } } - - public MapJoinBytesTableContainer.KeyValueHelper getWriteHelper() { - return writeHelper; + public KeyValuePut getKeyValuePutHelper() { + return keyValuePut; } public HashPartition[] getHashPartitions() { @@ -397,8 +427,8 @@ public long getMemoryThreshold() { public long refreshMemoryUsed() { long memUsed = 0; for (HashPartition hp : hashPartitions) { - if (hp.hashMap != null) { - memUsed += hp.hashMap.memorySize(); + if (hp.hashTable != null) { + memUsed += hp.hashTable.memorySize(); } } return memoryUsed = memUsed; @@ -424,41 +454,55 @@ public MapJoinKey putRow(MapJoinObjectSerDeContext keyContext, Writable currentK throws SerDeException, HiveException, IOException { SerDe keySerde = keyContext.getSerDe(), valSerde = valueContext.getSerDe(); - if (writeHelper == null) { + if (keyValuePut == null) { LOG.info("Initializing container with " + keySerde.getClass().getName() + " and " + valSerde.getClass().getName()); - // We assume this hashtable is loaded only when tez is enabled - LazyBinaryStructObjectInspector valSoi = - (LazyBinaryStructObjectInspector) valSerde.getObjectInspector(); - writeHelper = new MapJoinBytesTableContainer.LazyBinaryKvWriter(keySerde, valSoi, - valueContext.hasFilterTag()); - if (internalValueOi == null) { - internalValueOi = valSoi; - } - if (sortableSortOrders == null) { - sortableSortOrders = ((BinarySortableSerDe) keySerde).getSortOrders(); + if (mapJoinHashTableFactory.keyValuePutHelperIsExternal()) { + keyValuePut = mapJoinHashTableFactory.createKeyValuePutHelper(); + + // Not used. + internalValueOi = null; + sortableSortOrders = null; + } else { + // We assume this hashtable is loaded only when tez is enabled + LazyBinaryStructObjectInspector valSoi = + (LazyBinaryStructObjectInspector) valSerde.getObjectInspector(); + keyValuePut = new MapJoinBytesTableContainer.LazyBinaryKvWriter(keySerde, valSoi, + valueContext.hasFilterTag()); + if (internalValueOi == null) { + internalValueOi = valSoi; + } + if (sortableSortOrders == null) { + sortableSortOrders = ((BinarySortableSerDe) keySerde).getSortOrders(); + } } } - writeHelper.setKeyValue(currentKey, currentValue); - return internalPutRow(writeHelper, currentKey, currentValue); + return internalPutRow(keyValuePut, currentKey, currentValue); } - private MapJoinKey internalPutRow(KeyValueHelper keyValueHelper, + private MapJoinKey internalPutRow(KeyValuePut keyValuePut, Writable currentKey, Writable currentValue) throws SerDeException, IOException { // Next, put row into corresponding hash partition - int keyHash = keyValueHelper.getHashFromKey(); - int partitionId = keyHash & (hashPartitions.length - 1); + keyValuePut.setKeyValue(currentKey, currentValue); + int hashCode = keyValuePut.getKeyHashCode(); + + int partitionId = hashCode & (hashPartitions.length - 1); HashPartition hashPartition = hashPartitions[partitionId]; - bloom1.addLong(keyHash); + bloom1.addLong(hashCode); + if (useMinMax) { + long longValue = keyValuePut.getLongKey(); + longMin = Math.min(longValue, longMin); + longMax = Math.max(longValue, longMax); + } if (isOnDisk(partitionId) || isHashMapSpilledOnCreation(partitionId)) { KeyValueContainer kvContainer = hashPartition.getSidefileKVContainer(); kvContainer.add((HiveKey) currentKey, (BytesWritable) currentValue); } else { - hashPartition.hashMap.put(keyValueHelper, keyHash); // Pass along hashcode to avoid recalculation + hashPartition.hashTable.put(keyValuePut); totalInMemRowCount++; if ((totalInMemRowCount & (this.memoryCheckFrequency - 1)) == 0 && // check periodically @@ -498,7 +542,7 @@ public boolean isOnDisk(int partitionId) { /** * Check if the hash table of a specified partition has been "spilled" to disk when it was created. * In fact, in other words, check if a hashmap does exist or not. - * @param partitionId hashMap ID + * @param partitionId hashTable ID * @return true if it was not created at all, false if there is a hash table existing there */ public boolean isHashMapSpilledOnCreation(int partitionId) { @@ -527,7 +571,7 @@ private int biggestPartition() { if (isOnDisk(i)) { continue; } else { - size = hashPartitions[i].hashMap.getNumValues(); + size = hashPartitions[i].hashTable.getNumValues(); } if (size > maxSize) { maxSize = size; @@ -544,7 +588,7 @@ private int biggestPartition() { */ public long spillPartition(int partitionId) throws IOException { HashPartition partition = hashPartitions[partitionId]; - int inMemRowCount = partition.hashMap.getNumValues(); + int inMemRowCount = partition.hashTable.getNumValues(); Path path = Files.createTempFile("partition-" + partitionId + "-", null); OutputStream outputStream = Files.newOutputStream(path); @@ -552,7 +596,8 @@ public long spillPartition(int partitionId) throws IOException { com.esotericsoftware.kryo.io.Output output = new com.esotericsoftware.kryo.io.Output(outputStream); Kryo kryo = Utilities.runtimeSerializationKryo.get(); - kryo.writeObject(output, partition.hashMap); // use Kryo to serialize hashmap + kryo.writeObject(output, partition.hashTable); // use Kryo to serialize hashmap + partition.hashTableClass = partition.hashTable.getClass(); // remember so we can defrost it. output.close(); outputStream.close(); @@ -560,16 +605,16 @@ public long spillPartition(int partitionId) throws IOException { partition.hashMapOnDisk = true; LOG.info("Spilling hash partition " + partitionId + " (Rows: " + inMemRowCount + - ", Mem size: " + partition.hashMap.memorySize() + "): " + path); + ", Mem size: " + partition.hashTable.memorySize() + "): " + path); LOG.info("Memory usage before spilling: " + memoryUsed); - long memFreed = partition.hashMap.memorySize(); + long memFreed = partition.hashTable.memorySize(); memoryUsed -= memFreed; LOG.info("Memory usage after spilling: " + memoryUsed); partition.rowsOnDisk = inMemRowCount; totalInMemRowCount -= inMemRowCount; - partition.hashMap.clear(); + partition.hashTable.clear(); return memFreed; } @@ -667,25 +712,16 @@ public ReusableGetAdaptor createGetter(MapJoinKey keyTypeFromLoader) { public void seal() { for (HashPartition hp : hashPartitions) { // Only seal those partitions that haven't been spilled and cleared, - // because once a hashMap is cleared, it will become unusable - if (hp.hashMap != null && hp.hashMap.size() != 0) { - hp.hashMap.seal(); + // because once a hashTable is cleared, it will become unusable + if (hp.hashTable != null && hp.hashTable.size() != 0) { + hp.hashTable.seal(); } } } - - // Direct access interfaces. - - @Override - public void put(Writable currentKey, Writable currentValue) throws SerDeException, IOException { - directWriteHelper.setKeyValue(currentKey, currentValue); - internalPutRow(directWriteHelper, currentKey, currentValue); - } - /** Implementation of ReusableGetAdaptor that has Output for key serialization; row * container is also created once and reused for every row. */ - private class GetAdaptor implements ReusableGetAdaptor, ReusableGetAdaptorDirectAccess { + private class GetAdaptor implements ReusableGetAdaptor { private Object[] currentKey; private boolean[] nulls; @@ -700,9 +736,9 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromVector(VectorHashKeyWrapper kw, + public MapJoinResult setFromVector(VectorHashKeyWrapper kw, VectorExpressionWriter[] keyOutputWriters, VectorHashKeyWrapperBatch keyWrapperBatch) - throws HiveException { + throws HiveException, IOException { if (nulls == null) { nulls = new boolean[keyOutputWriters.length]; currentKey = new Object[keyOutputWriters.length]; @@ -722,8 +758,8 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromRow(Object row, List fields, - List ois) throws HiveException { + public MapJoinResult setFromRow(Object row, List fields, + List ois) throws HiveException, IOException { if (nulls == null) { nulls = new boolean[fields.size()]; currentKey = new Object[fields.size()]; @@ -737,7 +773,7 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromOther(ReusableGetAdaptor other) throws HiveException { + public MapJoinResult setFromOther(ReusableGetAdaptor other) throws HiveException, IOException { assert other instanceof GetAdaptor; GetAdaptor other2 = (GetAdaptor)other; nulls = other2.nulls; @@ -765,26 +801,13 @@ public MapJoinRowContainer getCurrentRows() { public Object[] getCurrentKey() { return currentKey; } - - // Direct access interfaces. - - @Override - public JoinUtil.JoinResult setDirect(byte[] bytes, int offset, int length, - BytesBytesMultiHashMap.Result hashMapResult) { - return currentValue.setDirect(bytes, offset, length, hashMapResult); - } - - @Override - public int directSpillPartitionId() { - return currentValue.directSpillPartitionId(); - } } /** Row container that gets and deserializes the rows on demand from bytes provided. */ private class ReusableRowContainer implements MapJoinRowContainer, AbstractRowContainer.RowIterator> { private byte aliasFilter; - private BytesBytesMultiHashMap.Result hashMapResult; + private MapJoinHashMapResult hashMapResult; /** * Sometimes, when container is empty in multi-table mapjoin, we need to add a dummy row. @@ -797,7 +820,7 @@ public int directSpillPartitionId() { private final boolean needsComplexObjectFixup; private final ArrayList complexObjectArrayBuffer; - private int partitionId; // Current hashMap in use + private int partitionId; // Current hashTable in use public ReusableRowContainer() { if (internalValueOi != null) { @@ -817,29 +840,29 @@ public ReusableRowContainer() { complexObjectArrayBuffer = null; } uselessIndirection = new ByteArrayRef(); - hashMapResult = new BytesBytesMultiHashMap.Result(); + hashMapResult = mapJoinHashTableFactory.createHashMapResult(); clearRows(); } /* Determine if there is a match between big table row and the corresponding hashtable * Three states can be returned: * MATCH: a match is found - * NOMATCH: no match is found from the specified partition + * NO_MATCH: no match is found from the specified partition * SPILL: the specified partition has been spilled to disk and is not available; * the evaluation for this big table row will be postponed. */ - public JoinUtil.JoinResult setFromOutput(Output output) throws HiveException { + public MapJoinResult setFromOutput(Output output) throws HiveException, IOException { int keyHash = HashCodeUtil.murmurHash(output.getData(), 0, output.getLength()); if (!bloom1.testLong(keyHash)) { /* * if the keyHash is missing in the bloom filter, then the value cannot - * exist in any of the spilled partition - return NOMATCH + * exist in any of the spilled partition - return NO_MATCH */ dummyRow = null; aliasFilter = (byte) 0xff; hashMapResult.forget(); - return JoinResult.NOMATCH; + return MapJoinResult.NO_MATCH; } partitionId = keyHash & (hashPartitions.length - 1); @@ -847,19 +870,25 @@ public ReusableRowContainer() { // If the target hash table is on disk, spill this row to disk as well to be processed later if (isOnDisk(partitionId)) { toSpillPartitionId = partitionId; - hashMapResult.forget(); - return JoinUtil.JoinResult.SPILL; + hashMapResult.setSpill(partitionId); + return MapJoinResult.SPILL; } else { - aliasFilter = hashPartitions[partitionId].hashMap.getValueResult(output.getData(), 0, - output.getLength(), hashMapResult); + hashPartitions[partitionId].hashTable.hashMapLookup(output.getData(), 0, output.getLength(), + keyHash, hashMapResult); + MapJoinResult mapJoinResult = hashMapResult.getMapJoinResult(); dummyRow = null; - if (hashMapResult.hasRows()) { - return JoinUtil.JoinResult.MATCH; - } else { + switch (mapJoinResult) { + case MATCH: + aliasFilter = hashMapResult.aliasFilter(); + break; + case NO_MATCH: aliasFilter = (byte) 0xff; - return JoinUtil.JoinResult.NOMATCH; + break; + default: + throw new RuntimeException("Unexpected map join result " + mapJoinResult.name()); } + return mapJoinResult; } } @@ -976,45 +1005,169 @@ public void addRow(Object[] value) { public void write(MapJoinObjectSerDeContext valueContext, ObjectOutputStream out) { throw new RuntimeException(this.getClass().getCanonicalName() + " cannot be serialized"); } + } - // Direct access. + @Override + public MapJoinHashTableFind getMapJoinHashTableFind() { + return (MapJoinHashTableFind) this; + } - public JoinUtil.JoinResult setDirect(byte[] bytes, int offset, int length, - BytesBytesMultiHashMap.Result hashMapResult) { + @Override + public MapJoinHashTableFactory getMapJoinHashTableFactory() { + return mapJoinHashTableFactory; + } - int keyHash = HashCodeUtil.murmurHash(bytes, offset, length); - partitionId = keyHash & (hashPartitions.length - 1); + @Override + public boolean useMinMax() { + return useMinMax; + } - if (!bloom1.testLong(keyHash)) { - /* - * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the - * spilled partition - return NOMATCH - */ - dummyRow = null; - aliasFilter = (byte) 0xff; - hashMapResult.forget(); - return JoinResult.NOMATCH; - } + @Override + public long min() { + return longMin; + } - // If the target hash table is on disk, spill this row to disk as well to be processed later - if (isOnDisk(partitionId)) { - return JoinUtil.JoinResult.SPILL; - } - else { - aliasFilter = hashPartitions[partitionId].hashMap.getValueResult(bytes, offset, length, - hashMapResult); - dummyRow = null; - if (hashMapResult.hasRows()) { - return JoinUtil.JoinResult.MATCH; - } else { - aliasFilter = (byte) 0xff; - return JoinUtil.JoinResult.NOMATCH; - } - } + @Override + public long max() { + return longMax; + } + + @Override + public void hashMapLookup(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMapResult hashMapResult) throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashMapResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashMapResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashMapLookup(keyBytes, keyStart, keyLength, + hashCode, hashMapResult); + } + } + + @Override + public void hashMapLookup(long key, int hashCode, MapJoinHashMapResult hashMapResult) + throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashMapResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashMapResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashMapLookup(key, + hashCode, hashMapResult); + } + } + + @Override + public void hashMultiSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) + throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashMultiSetResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashMultiSetResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashMultiSetContains(keyBytes, keyStart, keyLength, + hashCode, hashMultiSetResult); } + } - public int directSpillPartitionId() { - return partitionId; + @Override + public void hashMultiSetContains(long key, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashMultiSetResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashMultiSetResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashMultiSetContains(key, + hashCode, hashMultiSetResult); + } + } + + @Override + public void hashSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashSetResult hashSetResult) throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashSetResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashSetResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashSetContains(keyBytes, keyStart, keyLength, + hashCode, hashSetResult); + } + } + + @Override + public void hashSetContains(long key, int hashCode, MapJoinHashSetResult hashSetResult) + throws IOException { + + if (!bloom1.testLong(hashCode)) { + /* + * if the keyHash is missing in the bloom filter, then the value cannot exist in any of the + * spilled partition - return NOMATCH + */ + hashSetResult.setNoMatch(); + } + + int partitionId = hashCode & (hashPartitions.length - 1); + + // If the target hash table is on disk, spill this row to disk as well to be processed later + if (isOnDisk(partitionId)) { + hashSetResult.setSpill(partitionId); + } else { + hashPartitions[partitionId].hashTable.hashSetContains(key, + hashCode, hashSetResult); } } @@ -1022,8 +1175,8 @@ public int directSpillPartitionId() { public void dumpMetrics() { for (int i = 0; i < hashPartitions.length; i++) { HashPartition hp = hashPartitions[i]; - if (hp.hashMap != null) { - hp.hashMap.debugDumpMetrics(); + if (hp.hashTable != null) { + hp.hashTable.debugDumpMetrics(); } } } @@ -1053,4 +1206,5 @@ public int size() { } return totalSize; } + } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinBytesTableContainer.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinBytesTableContainer.java index 3dddee7..9465e3e 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinBytesTableContainer.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinBytesTableContainer.java @@ -19,6 +19,7 @@ package org.apache.hadoop.hive.ql.exec.persistence; +import java.io.IOException; import java.io.ObjectOutputStream; import java.util.ArrayList; import java.util.Collections; @@ -29,7 +30,13 @@ import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.conf.HiveConf; import org.apache.hadoop.hive.ql.exec.ExprNodeEvaluator; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePutWriter; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapper; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapperBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriter; @@ -64,14 +71,16 @@ /** * Table container that serializes keys and values using LazyBinarySerDe into - * BytesBytesMultiHashMap, with very low memory overhead. However, + * a MapJoinHashTableFind, with very low memory overhead. However, * there may be some perf overhead when retrieving rows. */ -public class MapJoinBytesTableContainer - implements MapJoinTableContainer, MapJoinTableContainerDirectAccess { +public class MapJoinBytesTableContainer implements MapJoinTableContainer { private static final Logger LOG = LoggerFactory.getLogger(MapJoinTableContainer.class); - private final BytesBytesMultiHashMap hashMap; + private final MapJoinHashTableFactory mapJoinHashTableFactory; + + private final MapJoinHashTable hashTable; + /** The OI used to deserialize values. We never deserialize keys. */ private LazyBinaryStructObjectInspector internalValueOi; /** @@ -81,31 +90,34 @@ * ordering. Hence, remember the ordering here; it is null if we do use LazyBinarySerDe. */ private boolean[] sortableSortOrders; - private KeyValueHelper writeHelper; - private DirectKeyValueWriter directWriteHelper; + private KeyValuePut keyValuePutHelper; private final List EMPTY_LIST = new ArrayList(0); public MapJoinBytesTableContainer(Configuration hconf, - MapJoinObjectSerDeContext valCtx, long keyCount, long memUsage) throws SerDeException { + MapJoinObjectSerDeContext valCtx, long keyCount, int maxProbeSize, + MapJoinHashTableFactory mapJoinHashTableFactory) throws SerDeException { this(HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEKEYCOUNTADJUSTMENT), HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLETHRESHOLD), HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEHASHTABLELOADFACTOR), HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEWBSIZE), - valCtx, keyCount, memUsage); + valCtx, keyCount, maxProbeSize, mapJoinHashTableFactory); } private MapJoinBytesTableContainer(float keyCountAdj, int threshold, float loadFactor, - int wbSize, MapJoinObjectSerDeContext valCtx, long keyCount, long memUsage) + int wbSize, MapJoinObjectSerDeContext valCtx, long keyCount, int maxProbeSize, + MapJoinHashTableFactory mapJoinHashTableFactory) throws SerDeException { int newThreshold = HashMapWrapper.calculateTableSize( keyCountAdj, threshold, loadFactor, keyCount); - hashMap = new BytesBytesMultiHashMap(newThreshold, loadFactor, wbSize, memUsage); - directWriteHelper = new DirectKeyValueWriter(); + this.mapJoinHashTableFactory = mapJoinHashTableFactory; + hashTable = mapJoinHashTableFactory.createHashTable(newThreshold, loadFactor, wbSize, maxProbeSize); } - public MapJoinBytesTableContainer(BytesBytesMultiHashMap hashMap) { - this.hashMap = hashMap; + public MapJoinBytesTableContainer(MapJoinHashTableFactory mapJoinHashTableFactory, + MapJoinHashTable hashTable) { + this.mapJoinHashTableFactory = mapJoinHashTableFactory; + this.hashTable = hashTable; } private LazyBinaryStructObjectInspector createInternalOi( @@ -136,13 +148,7 @@ public void setSortableSortOrders(boolean[] sortableSortOrders) { this.sortableSortOrders = sortableSortOrders; } - public static interface KeyValueHelper extends BytesBytesMultiHashMap.KvSource { - void setKeyValue(Writable key, Writable val) throws SerDeException; - /** Get hash value from the key. */ - int getHashFromKey() throws SerDeException; - } - - private static class KeyValueWriter implements KeyValueHelper { + private static class KeyValueWriter implements KeyValuePutWriter { private final SerDe keySerDe, valSerDe; private final StructObjectInspector keySoi, valSoi; private final List keyOis, valOis; @@ -202,12 +208,22 @@ public byte updateStateByte(Byte previousValue) { } @Override - public int getHashFromKey() throws SerDeException { + public boolean hasHashCode() { + return false; + } + + @Override + public int getKeyHashCode() throws SerDeException { + throw new UnsupportedOperationException("Not supported for MapJoinBytesTableContainer"); + } + + @Override + public long getLongKey() { throw new UnsupportedOperationException("Not supported for MapJoinBytesTableContainer"); } } - static class LazyBinaryKvWriter implements KeyValueHelper { + static class LazyBinaryKvWriter implements KeyValuePutWriter { private final LazyBinaryStruct.SingleFieldGetter filterGetter; private Writable key, value; private final SerDe keySerDe; @@ -240,7 +256,12 @@ public void writeKey(RandomAccessOutput dest) throws SerDeException { } @Override - public int getHashFromKey() throws SerDeException { + public boolean hasHashCode() { + return true; + } + + @Override + public int getKeyHashCode() throws SerDeException { if (!(key instanceof BinaryComparable)) { throw new SerDeException("Unexpected type " + key.getClass().getCanonicalName()); } @@ -301,72 +322,45 @@ public byte updateStateByte(Byte previousValue) { aliasFilter &= filterGetter.getShort(); return aliasFilter; } - } - - /* - * An implementation of KvSource that can handle key and value as BytesWritable objects. - */ - protected static class DirectKeyValueWriter implements KeyValueHelper { - - private BytesWritable key; - private BytesWritable val; - - @Override - public void setKeyValue(Writable key, Writable val) throws SerDeException { - this.key = (BytesWritable) key; - this.val = (BytesWritable) val; - } - - @Override - public void writeKey(RandomAccessOutput dest) throws SerDeException { - byte[] keyBytes = key.getBytes(); - int keyLength = key.getLength(); - dest.write(keyBytes, 0, keyLength); - } - - @Override - public void writeValue(RandomAccessOutput dest) throws SerDeException { - byte[] valueBytes = val.getBytes(); - int valueLength = val.getLength(); - dest.write(valueBytes, 0 , valueLength); - } - - @Override - public byte updateStateByte(Byte previousValue) { - // Not used by the direct access client -- native vector map join. - throw new UnsupportedOperationException("Updating the state by not supported"); - } @Override - public int getHashFromKey() throws SerDeException { - byte[] keyBytes = key.getBytes(); - int keyLength = key.getLength(); - return HashCodeUtil.murmurHash(keyBytes, 0, keyLength); + public long getLongKey() { + throw new UnsupportedOperationException("Not supported for MapJoinBytesTableContainer"); } } @SuppressWarnings("deprecation") @Override public MapJoinKey putRow(MapJoinObjectSerDeContext keyContext, Writable currentKey, - MapJoinObjectSerDeContext valueContext, Writable currentValue) throws SerDeException { + MapJoinObjectSerDeContext valueContext, Writable currentValue) + throws SerDeException, IOException { SerDe keySerde = keyContext.getSerDe(), valSerde = valueContext.getSerDe(); - if (writeHelper == null) { + if (keyValuePutHelper == null) { LOG.info("Initializing container with " + keySerde.getClass().getName() + " and " + valSerde.getClass().getName()); - if (keySerde instanceof BinarySortableSerDe && valSerde instanceof LazyBinarySerDe) { - LazyBinaryStructObjectInspector valSoi = - (LazyBinaryStructObjectInspector)valSerde.getObjectInspector(); - writeHelper = new LazyBinaryKvWriter(keySerde, valSoi, valueContext.hasFilterTag()); - internalValueOi = valSoi; - sortableSortOrders = ((BinarySortableSerDe)keySerde).getSortOrders(); - } else { - writeHelper = new KeyValueWriter(keySerde, valSerde, valueContext.hasFilterTag()); - internalValueOi = createInternalOi(valueContext); + if (mapJoinHashTableFactory.keyValuePutHelperIsExternal()) { + keyValuePutHelper = mapJoinHashTableFactory.createKeyValuePutHelper(); + + // Not used. + internalValueOi = null; sortableSortOrders = null; + } else { + if (keySerde instanceof BinarySortableSerDe && valSerde instanceof LazyBinarySerDe) { + LazyBinaryStructObjectInspector valSoi = + (LazyBinaryStructObjectInspector)valSerde.getObjectInspector(); + keyValuePutHelper = new LazyBinaryKvWriter(keySerde, valSoi, valueContext.hasFilterTag()); + internalValueOi = valSoi; + sortableSortOrders = ((BinarySortableSerDe)keySerde).getSortOrders(); + } else { + keyValuePutHelper = new KeyValueWriter(keySerde, valSerde, valueContext.hasFilterTag()); + internalValueOi = createInternalOi(valueContext); + sortableSortOrders = null; + } } } - writeHelper.setKeyValue(currentKey, currentValue); - hashMap.put(writeHelper, -1); + keyValuePutHelper.setKeyValue(currentKey, currentValue); + hashTable.put(keyValuePutHelper); + return null; // there's no key to return } @@ -390,15 +384,7 @@ public ReusableGetAdaptor createGetter(MapJoinKey keyTypeFromLoader) { @Override public void seal() { - hashMap.seal(); - } - - // Direct access interfaces. - - @Override - public void put(Writable currentKey, Writable currentValue) throws SerDeException { - directWriteHelper.setKeyValue(currentKey, currentValue); - hashMap.put(directWriteHelper, -1); + hashTable.seal(); } public static boolean hasComplexObjects(LazyBinaryStructObjectInspector lazyBinaryStructObjectInspector) { @@ -437,7 +423,7 @@ public static boolean hasComplexObjects(LazyBinaryStructObjectInspector lazyBina /** Implementation of ReusableGetAdaptor that has Output for key serialization; row * container is also created once and reused for every row. */ - private class GetAdaptor implements ReusableGetAdaptor, ReusableGetAdaptorDirectAccess { + private class GetAdaptor implements ReusableGetAdaptor { private Object[] currentKey; private boolean[] nulls; @@ -452,9 +438,9 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromVector(VectorHashKeyWrapper kw, + public MapJoinResult setFromVector(VectorHashKeyWrapper kw, VectorExpressionWriter[] keyOutputWriters, VectorHashKeyWrapperBatch keyWrapperBatch) - throws HiveException { + throws HiveException, IOException { if (nulls == null) { nulls = new boolean[keyOutputWriters.length]; currentKey = new Object[keyOutputWriters.length]; @@ -474,8 +460,8 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromRow(Object row, List fields, - List ois) throws HiveException { + public MapJoinResult setFromRow(Object row, List fields, + List ois) throws HiveException, IOException { if (nulls == null) { nulls = new boolean[fields.size()]; currentKey = new Object[fields.size()]; @@ -489,7 +475,7 @@ public GetAdaptor() { } @Override - public JoinUtil.JoinResult setFromOther(ReusableGetAdaptor other) { + public MapJoinResult setFromOther(ReusableGetAdaptor other) throws IOException { assert other instanceof GetAdaptor; GetAdaptor other2 = (GetAdaptor)other; nulls = other2.nulls; @@ -518,18 +504,6 @@ public MapJoinRowContainer getCurrentRows() { return currentKey; } - // Direct access interfaces. - - @Override - public JoinUtil.JoinResult setDirect(byte[] bytes, int offset, int length, - BytesBytesMultiHashMap.Result hashMapResult) { - return currentValue.setDirect(bytes, offset, length, hashMapResult); - } - - @Override - public int directSpillPartitionId() { - throw new UnsupportedOperationException("Getting the spill hash partition not supported"); - } } /** Row container that gets and deserializes the rows on demand from bytes provided. */ @@ -538,7 +512,7 @@ public int directSpillPartitionId() { private byte aliasFilter; /** Hash table wrapper specific to the container. */ - private BytesBytesMultiHashMap.Result hashMapResult; + private MapJoinHashMapResult hashMapResult; /** * Sometimes, when container is empty in multi-table mapjoin, we need to add a dummy row. @@ -569,21 +543,28 @@ public ReusableRowContainer() { complexObjectArrayBuffer = null; } uselessIndirection = new ByteArrayRef(); - hashMapResult = new BytesBytesMultiHashMap.Result(); + hashMapResult = mapJoinHashTableFactory.createHashMapResult(); clearRows(); } - public JoinUtil.JoinResult setFromOutput(Output output) { + public MapJoinResult setFromOutput(Output output) throws IOException { + + int keyHash = HashCodeUtil.murmurHash(output.getData(), 0, output.getLength()); - aliasFilter = hashMap.getValueResult( - output.getData(), 0, output.getLength(), hashMapResult); + hashTable.hashMapLookup(output.getData(), 0, output.getLength(), keyHash, hashMapResult); + MapJoinResult mapJoinResult = hashMapResult.getMapJoinResult(); dummyRow = null; - if (hashMapResult.hasRows()) { - return JoinUtil.JoinResult.MATCH; - } else { + switch (mapJoinResult) { + case MATCH: + aliasFilter = hashMapResult.aliasFilter(); + break; + case NO_MATCH: aliasFilter = (byte) 0xff; - return JoinUtil.JoinResult.NOMATCH; + break; + default: + throw new RuntimeException("Unexpected map join result " + mapJoinResult.name()); } + return mapJoinResult; } @@ -700,20 +681,6 @@ public void addRow(Object[] value) { public void write(MapJoinObjectSerDeContext valueContext, ObjectOutputStream out) { throw new RuntimeException(this.getClass().getCanonicalName() + " cannot be serialized"); } - - // Direct access. - - public JoinUtil.JoinResult setDirect(byte[] bytes, int offset, int length, - BytesBytesMultiHashMap.Result hashMapResult) { - aliasFilter = hashMap.getValueResult(bytes, offset, length, hashMapResult); - dummyRow = null; - if (hashMapResult.hasRows()) { - return JoinUtil.JoinResult.MATCH; - } else { - aliasFilter = (byte) 0xff; - return JoinUtil.JoinResult.NOMATCH; - } - } } public static boolean isSupportedKey(ObjectInspector keyOi) { @@ -728,7 +695,7 @@ public static boolean isSupportedKey(ObjectInspector keyOi) { @Override public void dumpMetrics() { - hashMap.debugDumpMetrics(); + hashTable.debugDumpMetrics(); } @Override @@ -738,6 +705,16 @@ public boolean hasSpill() { @Override public int size() { - return hashMap.size(); + return hashTable.size(); + } + + @Override + public MapJoinHashTable getMapJoinHashTableFind() { + return hashTable; + } + + @Override + public MapJoinHashTableFactory getMapJoinHashTableFactory() { + return mapJoinHashTableFactory; } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainer.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainer.java index 869aefd..842af93 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainer.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainer.java @@ -22,7 +22,9 @@ import java.util.List; import org.apache.hadoop.hive.ql.exec.ExprNodeEvaluator; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapper; import org.apache.hadoop.hive.ql.exec.vector.VectorHashKeyWrapperBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriter; @@ -42,21 +44,21 @@ * Changes current rows to which adaptor is referring to the rows corresponding to * the key represented by a VHKW object, and writers and batch used to interpret it. */ - JoinUtil.JoinResult setFromVector(VectorHashKeyWrapper kw, VectorExpressionWriter[] keyOutputWriters, - VectorHashKeyWrapperBatch keyWrapperBatch) throws HiveException; + MapJoinResult setFromVector(VectorHashKeyWrapper kw, VectorExpressionWriter[] keyOutputWriters, + VectorHashKeyWrapperBatch keyWrapperBatch) throws HiveException, IOException; /** * Changes current rows to which adaptor is referring to the rows corresponding to * the key represented by a row object, and fields and ois used to interpret it. */ - JoinUtil.JoinResult setFromRow(Object row, List fields, List ois) - throws HiveException; + MapJoinResult setFromRow(Object row, List fields, List ois) + throws HiveException, IOException; /** * Changes current rows to which adaptor is referring to the rows corresponding to * the key that another adaptor has already deserialized via setFromVector/setFromRow. */ - JoinUtil.JoinResult setFromOther(ReusableGetAdaptor other) throws HiveException; + MapJoinResult setFromOther(ReusableGetAdaptor other) throws HiveException, IOException; /** * Checks whether the current key has any nulls. @@ -94,6 +96,14 @@ MapJoinKey putRow(MapJoinObjectSerDeContext keyContext, Writable currentKey, */ ReusableGetAdaptor createGetter(MapJoinKey keyTypeFromLoader); + /** + * Provides "managed" access to the map join hash table. For use by the native vector map join + * implementation. + */ + MapJoinHashTableFind getMapJoinHashTableFind(); + + MapJoinHashTableFactory getMapJoinHashTableFactory(); + /** Clears the contents of the table. */ void clear(); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerDirectAccess.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerDirectAccess.java deleted file mode 100644 index 2164d38..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerDirectAccess.java +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.persistence; - - -import java.io.IOException; - -import org.apache.hadoop.hive.serde2.SerDeException; -import org.apache.hadoop.io.Writable; - -public interface MapJoinTableContainerDirectAccess { - - void put(Writable currentKey, Writable currentValue) throws SerDeException, IOException; - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerSerDe.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerSerDe.java index d6deabe..eaa2a89 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerSerDe.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/MapJoinTableContainerSerDe.java @@ -32,7 +32,6 @@ import org.apache.hadoop.fs.Path; import org.apache.hadoop.hive.common.JavaUtils; import org.apache.hadoop.hive.conf.HiveConf; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast.VectorMapJoinFastTableContainer; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.MapJoinDesc; import org.apache.hadoop.hive.serde2.SerDe; @@ -140,7 +139,8 @@ public MapJoinTableContainer load( Map metaData = (Map) in.readObject(); if (tableContainer == null) { tableContainer = useOptimizedContainer ? - new MapJoinBytesTableContainer(hconf, valueContext, -1, 0) : + new MapJoinBytesTableContainer(hconf, valueContext, -1, 0, + new BytesBytesMultiHashMapFactory()) : create(name, metaData); } if (useOptimizedContainer) { @@ -197,74 +197,6 @@ private void loadOptimized(MapJoinBytesTableContainer container, ObjectInputStre } } - /** - * Loads the small table into a VectorMapJoinFastTableContainer. Only used on Spark path. - * @param mapJoinDesc The descriptor for the map join - * @param fs FileSystem of the folder. - * @param folder The folder to load table container. - * @param hconf The hive configuration - * @return Loaded table. - */ - @SuppressWarnings("unchecked") - public MapJoinTableContainer loadFastContainer(MapJoinDesc mapJoinDesc, - FileSystem fs, Path folder, Configuration hconf) throws HiveException { - try { - if (!fs.isDirectory(folder)) { - throw new HiveException("Error, not a directory: " + folder); - } - FileStatus[] fileStatuses = fs.listStatus(folder); - if (fileStatuses == null || fileStatuses.length == 0) { - return null; - } - - SerDe keySerDe = keyContext.getSerDe(); - SerDe valueSerDe = valueContext.getSerDe(); - Writable key = keySerDe.getSerializedClass().newInstance(); - Writable value = valueSerDe.getSerializedClass().newInstance(); - - VectorMapJoinFastTableContainer tableContainer = - new VectorMapJoinFastTableContainer(mapJoinDesc, hconf, -1); - - for (FileStatus fileStatus : fileStatuses) { - Path filePath = fileStatus.getPath(); - if (ShimLoader.getHadoopShims().isDirectory(fileStatus)) { - throw new HiveException("Error, not a file: " + filePath); - } - InputStream is = null; - ObjectInputStream in = null; - try { - is = fs.open(filePath, 4096); - in = new ObjectInputStream(is); - // skip the name and metadata - in.readUTF(); - in.readObject(); - int numKeys = in.readInt(); - for (int keyIndex = 0; keyIndex < numKeys; keyIndex++) { - key.readFields(in); - long numRows = in.readLong(); - for (long rowIndex = 0L; rowIndex < numRows; rowIndex++) { - value.readFields(in); - tableContainer.putRow(null, key, null, value); - } - } - } finally { - if (in != null) { - in.close(); - } else if (is != null) { - is.close(); - } - } - } - - tableContainer.seal(); - return tableContainer; - } catch (IOException e) { - throw new HiveException("IO error while trying to create table container", e); - } catch (Exception e) { - throw new HiveException("Error while trying to create table container", e); - } - } - public void persist(ObjectOutputStream out, MapJoinPersistableTableContainer tableContainer) throws HiveException { int numKeys = tableContainer.size(); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/ReusableGetAdaptorDirectAccess.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/ReusableGetAdaptorDirectAccess.java deleted file mode 100644 index 0685d84..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/ReusableGetAdaptorDirectAccess.java +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.persistence; - - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; - -public interface ReusableGetAdaptorDirectAccess { - - JoinResult setDirect(byte[] bytes, int offset, int length, - BytesBytesMultiHashMap.Result hashMapResult); - - int directSpillPartitionId(); -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMapResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMapResult.java new file mode 100644 index 0000000..b33e623 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMapResult.java @@ -0,0 +1,73 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; + +/* + * Interface for a hash map result. For reading the values, one-by-one. + */ +public interface MapJoinHashMapResult extends MapJoinHashTableResult { + + /** + * @return Whether there are any rows (i.e. true for match). + */ + boolean hasRows(); + + /** + * @return Whether there is 1 value row. + */ + boolean isSingleRow(); + + /** + * @return Whether there is a capped count available from cappedCount. + */ + boolean isCappedCountAvailable(); + + /** + * @return The count of values, up to a arbitrary cap limit. When available, the capped + * count can be used to make decisions on how to optimally generate join results. + */ + int cappedCount(); + + /** + * @return A reference to the first value, or null if there are no values. + */ + ByteSegmentRef first(); + + /** + * @return The next value, or null if there are no more values to be read. + */ + ByteSegmentRef next(); + + /** + * @return Whether reading is at the end. + */ + boolean isEof(); + + /** + * @return Whether there is alias filter available from aliasFilter. + */ + boolean isAliasFilterAvailable(); + + /** + * @return Alias filter byte. + */ + byte aliasFilter(); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResult.java new file mode 100644 index 0000000..04b1192 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResult.java @@ -0,0 +1,32 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Abstract class for a hash multi-set result additional methods. + */ +public interface MapJoinHashMultiSetResult extends MapJoinHashTableResult { + + /* + * @return The multi-set count for the lookup key. + */ + public long count(); + + public void setMatch(long count); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResultImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResultImpl.java new file mode 100644 index 0000000..b568163 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashMultiSetResultImpl.java @@ -0,0 +1,45 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Abstract class for a hash multi-set result. + */ +public class MapJoinHashMultiSetResultImpl extends MapJoinHashTableResultImpl + implements MapJoinHashMultiSetResult { + + protected long count; + + public MapJoinHashMultiSetResultImpl() { + super(); + count = 0; + } + + /* + * @return The multi-set count for the lookup key. + */ + public long count() { + return count; + } + + public void setMatch(long count) { + this.count = count; + mapJoinResult = MapJoinResult.MATCH; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResult.java new file mode 100644 index 0000000..20f989a --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResult.java @@ -0,0 +1,28 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Interface for a hash set result additional methods. + */ +public interface MapJoinHashSetResult extends MapJoinHashTableResult { + + void setMatch(); + +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResultImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResultImpl.java new file mode 100644 index 0000000..18037b6 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashSetResultImpl.java @@ -0,0 +1,35 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Abstract class for a hash set result. + */ +public class MapJoinHashSetResultImpl extends MapJoinHashTableResultImpl + implements MapJoinHashSetResult { + + public MapJoinHashSetResultImpl() { + super(); + } + + public void setMatch() { + mapJoinResult = MapJoinResult.MATCH; + } + +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTable.java new file mode 100644 index 0000000..0d6cae9 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTable.java @@ -0,0 +1,27 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public interface MapJoinHashTable extends MapJoinHashTableManage, MapJoinHashTableFind { + +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFactory.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFactory.java new file mode 100644 index 0000000..b2b81cb --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFactory.java @@ -0,0 +1,77 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public interface MapJoinHashTableFactory { + + /** + * @return true when the hash table manages its the key value put helper. + */ + public boolean keyValuePutHelperIsExternal(); + + /** + * @return true if min/max optimization could be used. + */ + public boolean useMinMax(); + + /** + * @return For hash tables has manage their own helper, a new key value put helper object. + */ + KeyValuePut createKeyValuePutHelper(); + + /** + * @param initialCapacity + * @param loadFactor + * @param writeBuffersSize + * @param maxProbeSize + * @return A new hash table. + */ + MapJoinHashTable createHashTable(int initialCapacity, float loadFactor, + int writeBuffersSize, int maxProbeSize); + + /* + * @return A new hash map result implementation specific object. + * + * The object can be used to access the values when there is a match, or + * access spill information when the partition with the key is currently spilled. + */ + MapJoinHashMapResult createHashMapResult(); + + /* + * @return A new hash multi-set result implementation specific object. + * + * The object can be used to access the *count* of values when the key is contained in the + * multi-set, or access spill information when the partition with the key is currently spilled. + */ + MapJoinHashMultiSetResult createHashMultiSetResult(); + + /* + * @return A new hash set result implementation specific object. + * + * The object can be used to access access spill information when the partition with the key + * is currently spilled. + */ + MapJoinHashSetResult createHashSetResult(); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFind.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFind.java new file mode 100644 index 0000000..936d99a --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableFind.java @@ -0,0 +1,157 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +import java.io.IOException; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public interface MapJoinHashTableFind { + + //---------------------------- COMMON LONG METHODS (Begin)---------------------------------------- + + boolean useMinMax(); + + long min(); + long max(); + + //----------------------------- COMMON LONG METHODS (End)----------------------------------------- + + //-------------------------------- HASH MAP (Begin)----------------------------------------------- + + /* + * Lookup a byte array key in the hash map. + * + * @param keyBytes + * A byte array containing the key within a range. + * @param keyStart + * The offset the beginning of the key. + * @param keyLength + * The length of the key. + * @param hashCode + * The key hash code. + * @param hashMapResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashMapLookup(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMapResult hashMapResult) throws IOException; + + /* + * Lookup an long in the hash map. + * + * @param key + * The long key. + * @param hashCode + * The key hash code. + * @param hashMapResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashMapLookup(long key, int hashCode, + MapJoinHashMapResult hashMapResult) throws IOException; + + + //-------------------------------- HASH MAP (End) ------------------------------------------------ + + //---------------------------- HASH MULTI-SET (Begin) ------------------------------------------- + + /* + * Lookup an byte array key in the hash multi-set. + * + * @param keyBytes + * A byte array containing the key within a range. + * @param keyStart + * The offset the beginning of the key. + * @param keyLength + * The length of the key. + * @param hashCode + * The key hash code. + * @param hashMultiSetResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashMultiSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) + throws IOException; + + /* + * Lookup an long in the hash multi-set. + * + * @param key + * The long key. + * @param hashCode + * The key hash code. + * @param hashMultiSetResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashMultiSetContains(long key, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) + throws IOException; + + + //----------------------------- HASH MULTI-SET (End) -------------------------------------------- + + //------------------------------- HASH SET (Begin) ---------------------------------------------- + + /* + * Lookup an byte array key in the hash set. + * + * @param keyBytes + * A byte array containing the key within a range. + * @param keyStart + * The offset the beginning of the key. + * @param keyLength + * The length of the key. + * @param hashCode + * The key hash code. + * @param hashSetResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashSetResult hashSetResult) + throws IOException; + + /* + * Lookup an long in the hash set. + * + * @param key + * The long key. + * @param hashCode + * The key hash code. + * @param hashSetResult + * The object to receive small table value(s) information on a MATCH. + * Or, for SPILL, it has information on where to spill the big table row. + * Examine mapJoinResult() for lookup result. + */ + void hashSetContains(long key, int hashCode, MapJoinHashSetResult hashSetResult) + throws IOException; + + //--------------------------------- HASH SET (End) ---------------------------------------------- + +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableManage.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableManage.java new file mode 100644 index 0000000..ae5c8fe --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableManage.java @@ -0,0 +1,94 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +import java.io.IOException; + +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.ByteStream.RandomAccessOutput; +import org.apache.hadoop.io.Writable; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public interface MapJoinHashTableManage { + + public static interface KeyValuePut { + + void setKeyValue(Writable key, Writable value) throws SerDeException, IOException; + + boolean hasHashCode(); + + int getKeyHashCode() throws SerDeException; + + long getLongKey(); + } + + /** + * The source of keys and values to put into hashtable; avoids byte copying. + * Supports BytesBytesMultiHashMap. + */ + public static interface KeyValuePutWriter extends KeyValuePut { + /** Write key into output. */ + void writeKey(RandomAccessOutput dest) throws SerDeException; + + /** Write value into output. */ + void writeValue(RandomAccessOutput dest) throws SerDeException; + + /** + * Provide updated value for state byte for a key. + * @param previousValue Previous value; null if this is the first call per key. + * @return The updated value. + */ + byte updateStateByte(Byte previousValue); + } + + public void put(KeyValuePut keyValuePutHelper) throws SerDeException; + + + /** + * Number of keys in the hashmap + * @return number of keys + */ + int size(); + + /** + * Number of values in the hashmap + * This is equal to or bigger than number of keys, since some values may share the same key + * @return number of values + */ + int getNumValues(); + + /** + * Number of bytes used by the hashmap + * There are two main components that take most memory: writeBuffers and refs + * Others include instance fields: 100 + * @return number of bytes + */ + long memorySize(); + + void seal(); + + void clear(); + + void debugDumpMetrics(); + + void expandAndRehashToTarget(int estimateNewRowCount); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResult.java new file mode 100644 index 0000000..591b0da --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResult.java @@ -0,0 +1,60 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Root interface for a hash table result. + */ +public interface MapJoinHashTableResult { + + /** + * Represents the hash map lookup result between two tables + */ + public enum MapJoinResult { + NONE, + MATCH, + NO_MATCH, + SPILL + } + + /** + * Forget about the most recent hash table lookup or contains call. + */ + void forget(); + + /** + * Set the map join result. + */ + void setNoMatch(); + + /** + * @return The map join result. + */ + MapJoinResult getMapJoinResult(); + + /** + * Set the spill partition id. + */ + void setSpill(int spillPartitionId); + + /** + * @return The Hybrid Grace spill partition id. + */ + int getSpillPartitionId(); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResultImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResultImpl.java new file mode 100644 index 0000000..f48945a --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/persistence/mapjoinhashtable/MapJoinHashTableResultImpl.java @@ -0,0 +1,87 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable; + +/* + * Root abstract class for a hash table result. + */ +public abstract class MapJoinHashTableResultImpl implements MapJoinHashTableResult { + + protected MapJoinResult mapJoinResult; + + private int spillPartitionId; + + public MapJoinHashTableResultImpl() { + mapJoinResult = MapJoinResult.NONE; + spillPartitionId = -1; + } + + /** + * Forget about the most recent hash table lookup or contains call. + */ + @Override + public void forget() { + mapJoinResult = MapJoinResult.NONE; + spillPartitionId = -1; + } + + /** + * Set the map join result. + */ + @Override + public void setNoMatch() { + this.mapJoinResult = MapJoinResult.NO_MATCH; + } + + /** + * @return The map join result. + */ + @Override + public MapJoinResult getMapJoinResult() { + return mapJoinResult; + } + + /** + * Set the spill partition id. + */ + @Override + public void setSpill(int spillPartitionId) { + this.mapJoinResult = MapJoinResult.SPILL; + this.spillPartitionId = spillPartitionId; + } + + /** + * @return The Hybrid Grace spill partition id. + */ + @Override + public int getSpillPartitionId() { + return spillPartitionId; + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(mapJoinResult.name()); + if (mapJoinResult == MapJoinResult.SPILL) { + sb.append(", spillPartitionId "); + sb.append(spillPartitionId); + } + return sb.toString(); + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/spark/HashTableLoader.java ql/src/java/org/apache/hadoop/hive/ql/exec/spark/HashTableLoader.java index 7ada611..2eddbf9 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/spark/HashTableLoader.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/spark/HashTableLoader.java @@ -63,8 +63,6 @@ private MapJoinOperator joinOp; private MapJoinDesc desc; - private boolean useFastContainer = false; - @Override public void init(ExecMapperContext context, MapredContext mrContext, Configuration hconf, MapJoinOperator joinOp) { @@ -72,12 +70,6 @@ public void init(ExecMapperContext context, MapredContext mrContext, Configurati this.hconf = hconf; this.joinOp = joinOp; this.desc = joinOp.getConf(); - if (desc.getVectorMode() && HiveConf.getBoolVar( - hconf, HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_FAST_HASHTABLE_ENABLED)) { - VectorMapJoinDesc vectorDesc = desc.getVectorDesc(); - useFastContainer = vectorDesc != null && vectorDesc.hashTableImplementationType() == - VectorMapJoinDesc.HashTableImplementationType.FAST; - } } @Override @@ -107,7 +99,7 @@ public void load(MapJoinTableContainer[] mapJoinTables, FileSystem fs = FileSystem.get(baseDir.toUri(), hconf); BucketMapJoinContext mapJoinCtx = localWork.getBucketMapjoinContext(); boolean firstContainer = true; - boolean useOptimizedContainer = !useFastContainer && HiveConf.getBoolVar( + boolean useOptimizedContainer = HiveConf.getBoolVar( hconf, HiveConf.ConfVars.HIVEMAPJOINUSEOPTIMIZEDTABLE); for (int pos = 0; pos < mapJoinTables.length; pos++) { if (pos == desc.getPosBigTable() || mapJoinTables[pos] != null) { @@ -155,17 +147,14 @@ private MapJoinTableContainer load(FileSystem fs, Path path, MapJoinTableContainerSerDe mapJoinTableSerde) throws HiveException { LOG.info("\tLoad back all hashtable files from tmp folder uri:" + path); if (!SparkUtilities.isDedicatedCluster(hconf)) { - return useFastContainer ? mapJoinTableSerde.loadFastContainer(desc, fs, path, hconf) : - mapJoinTableSerde.load(fs, path, hconf); + return mapJoinTableSerde.load(fs, path, hconf); } MapJoinTableContainer mapJoinTable = SmallTableCache.get(path); if (mapJoinTable == null) { synchronized (path.toString().intern()) { mapJoinTable = SmallTableCache.get(path); if (mapJoinTable == null) { - mapJoinTable = useFastContainer ? - mapJoinTableSerde.loadFastContainer(desc, fs, path, hconf) : - mapJoinTableSerde.load(fs, path, hconf); + mapJoinTable = mapJoinTableSerde.load(fs, path, hconf); SmallTableCache.cache(path, mapJoinTable); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/tez/HashTableLoader.java ql/src/java/org/apache/hadoop/hive/ql/exec/tez/HashTableLoader.java index ff79110..dfe430e 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/tez/HashTableLoader.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/tez/HashTableLoader.java @@ -31,6 +31,7 @@ import org.apache.hadoop.hive.ql.exec.MapJoinOperator; import org.apache.hadoop.hive.ql.exec.MapredContext; import org.apache.hadoop.hive.ql.exec.mr.ExecMapperContext; +import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMapFactory; import org.apache.hadoop.hive.ql.exec.persistence.HashMapWrapper; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableConf; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer; @@ -38,9 +39,13 @@ import org.apache.hadoop.hive.ql.exec.persistence.MapJoinObjectSerDeContext; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainerSerDe; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast.VectorMapJoinFastHashTableFactory; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.ExprNodeDesc; import org.apache.hadoop.hive.ql.plan.MapJoinDesc; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableImplementationType; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector.Category; import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector; @@ -63,12 +68,34 @@ private MapJoinDesc desc; private TezContext tezContext; + private VectorMapJoinDesc vectorDesc; + private MapJoinHashTableFactory mapJoinHashTableFactory; + @Override public void init(ExecMapperContext context, MapredContext mrContext, Configuration hconf, MapJoinOperator joinOp) { this.tezContext = (TezContext) mrContext; this.hconf = hconf; this.desc = joinOp.getConf(); + this.vectorDesc = this.desc.getVectorDesc(); + HashTableImplementationType hashTableImplementationType; + if (this.vectorDesc == null) { + hashTableImplementationType = HashTableImplementationType.NONE; + } else { + hashTableImplementationType = vectorDesc.hashTableImplementationType(); + } + switch (hashTableImplementationType) { + case NONE: + // Non-native vector map join uses the regular BytesBytesMultiHashMap. + mapJoinHashTableFactory = new BytesBytesMultiHashMapFactory(); + break; + case FAST: + mapJoinHashTableFactory = new VectorMapJoinFastHashTableFactory(this.desc); + break; + default: + throw new RuntimeException("Unknown vector map join hash table implementation type " + + hashTableImplementationType.name()); + } } @Override @@ -166,7 +193,9 @@ public void load(MapJoinTableContainer[] mapJoinTables, KeyValueReader kvReader = (KeyValueReader) input.getReader(); MapJoinObjectSerDeContext keyCtx = mapJoinTableSerdes[pos].getKeyContext(), valCtx = mapJoinTableSerdes[pos].getValueContext(); - if (useOptimizedTables) { + if (useOptimizedTables && + mapJoinHashTableFactory instanceof BytesBytesMultiHashMapFactory) { + // Some keys are not supported by regular hive key handling. ObjectInspector keyOi = keyCtx.getSerDe().getObjectInspector(); if (!MapJoinBytesTableContainer.isSupportedKey(keyOi)) { if (isFirstKey) { @@ -195,10 +224,11 @@ public void load(MapJoinTableContainer[] mapJoinTables, MapJoinTableContainer tableContainer; if (useOptimizedTables) { if (!useHybridGraceHashJoin || isCrossProduct) { - tableContainer = new MapJoinBytesTableContainer(hconf, valCtx, keyCount, 0); + tableContainer = new MapJoinBytesTableContainer(hconf, valCtx, keyCount, 0, + mapJoinHashTableFactory); } else { tableContainer = new HybridHashTableContainer(hconf, keyCount, memory, - desc.getParentDataSizes().get(pos), nwayConf); + desc.getParentDataSizes().get(pos), nwayConf, mapJoinHashTableFactory); } } else { tableContainer = new HashMapWrapper(hconf, keyCount); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnMapping.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnMapping.java index c4b95c3..c890674 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnMapping.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnMapping.java @@ -20,6 +20,8 @@ import java.util.Arrays; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; + /** * This class collects column information for copying a row from one VectorizedRowBatch to * same/another batch. @@ -30,7 +32,7 @@ protected int[] sourceColumns; protected int[] outputColumns; - protected String[] typeNames; + protected TypeInfo[] typeInfos; protected VectorColumnOrderedMap vectorColumnMapping; @@ -38,7 +40,7 @@ public VectorColumnMapping(String name) { this.vectorColumnMapping = new VectorColumnOrderedMap(name); } - public abstract void add(int sourceColumn, int outputColumn, String typeName); + public abstract void add(int sourceColumn, int outputColumn, TypeInfo typeInfo); public abstract void finalize(); @@ -54,8 +56,8 @@ public int getCount() { return outputColumns; } - public String[] getTypeNames() { - return typeNames; + public TypeInfo[] getTypeInfos() { + return typeInfos; } @Override @@ -65,7 +67,7 @@ public String toString() { sb.append(", "); sb.append("output columns: " + Arrays.toString(outputColumns)); sb.append(", "); - sb.append("type names: " + Arrays.toString(typeNames)); + sb.append("type infos: " + Arrays.toString(typeInfos)); return sb.toString(); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOrderedMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOrderedMap.java index 0e6014b..9a0c3f7 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOrderedMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOrderedMap.java @@ -23,8 +23,10 @@ import java.util.TreeMap; import org.apache.commons.lang.ArrayUtils; + import org.slf4j.Logger; import org.slf4j.LoggerFactory; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; /** * This class collects column information for mapping vector columns, including the hive type name. @@ -43,17 +45,17 @@ private class Value { int valueColumn; - String typeName; + TypeInfo typeInfo; - Value(int valueColumn, String typeName) { + Value(int valueColumn, TypeInfo typeInfo) { this.valueColumn = valueColumn; - this.typeName = typeName; + this.typeInfo = typeInfo; } public String toString() { StringBuilder sb = new StringBuilder(); sb.append("(value column: " + valueColumn); - sb.append(", type name: " + typeName + ")"); + sb.append(", type info: " + typeInfo.toString() + ")"); return sb.toString(); } } @@ -62,12 +64,12 @@ public String toString() { private final int[] orderedColumns; private final int[] valueColumns; - private final String[] typeNames; + private final TypeInfo[] typeInfos; - Mapping(int[] orderedColumns, int[] valueColumns, String[] typeNames) { + Mapping(int[] orderedColumns, int[] valueColumns, TypeInfo[] typeInfos) { this.orderedColumns = orderedColumns; this.valueColumns = valueColumns; - this.typeNames = typeNames; + this.typeInfos = typeInfos; } public int getCount() { @@ -82,8 +84,8 @@ public int getCount() { return valueColumns; } - public String[] getTypeNames() { - return typeNames; + public TypeInfo[] getTypeInfos() { + return typeInfos; } } @@ -92,14 +94,14 @@ public VectorColumnOrderedMap(String name) { orderedTreeMap = new TreeMap(); } - public void add(int orderedColumn, int valueColumn, String typeName) { + public void add(int orderedColumn, int valueColumn, TypeInfo typeInfo) { if (orderedTreeMap.containsKey(orderedColumn)) { throw new RuntimeException( name + " duplicate column " + orderedColumn + " in ordered column map " + orderedTreeMap.toString() + - " when adding value column " + valueColumn + ", type " + typeName); + " when adding value column " + valueColumn + ", type into " + typeInfo.toString()); } - orderedTreeMap.put(orderedColumn, new Value(valueColumn, typeName)); + orderedTreeMap.put(orderedColumn, new Value(valueColumn, typeInfo)); } public boolean orderedColumnsContain(int orderedColumn) { @@ -109,17 +111,17 @@ public boolean orderedColumnsContain(int orderedColumn) { public Mapping getMapping() { ArrayList orderedColumns = new ArrayList(); ArrayList valueColumns = new ArrayList(); - ArrayList typeNames = new ArrayList(); + ArrayList typeInfos = new ArrayList(); for (Map.Entry entry : orderedTreeMap.entrySet()) { orderedColumns.add(entry.getKey()); Value value = entry.getValue(); valueColumns.add(value.valueColumn); - typeNames.add(value.typeName); + typeInfos.add(value.typeInfo); } return new Mapping( ArrayUtils.toPrimitive(orderedColumns.toArray(new Integer[0])), ArrayUtils.toPrimitive(valueColumns.toArray(new Integer[0])), - typeNames.toArray(new String[0])); + typeInfos.toArray(new TypeInfo[0])); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOutputMapping.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOutputMapping.java index f35aff7..4ceff6b 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOutputMapping.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnOutputMapping.java @@ -19,6 +19,7 @@ package org.apache.hadoop.hive.ql.exec.vector; import org.apache.hadoop.hive.ql.exec.vector.VectorColumnOrderedMap.Mapping; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; /** * This class collects column information for copying a row from one VectorizedRowBatch to @@ -35,9 +36,9 @@ public VectorColumnOutputMapping(String name) { } @Override - public void add(int sourceColumn, int outputColumn, String typeName) { + public void add(int sourceColumn, int outputColumn, TypeInfo typeInfo) { // Order on outputColumn. - vectorColumnMapping.add(outputColumn, sourceColumn, typeName); + vectorColumnMapping.add(outputColumn, sourceColumn, typeInfo); } public boolean containsOutputColumn(int outputColumn) { @@ -51,7 +52,7 @@ public void finalize() { // Ordered columns are the output columns. sourceColumns = mapping.getValueColumns(); outputColumns = mapping.getOrderedColumns(); - typeNames = mapping.getTypeNames(); + typeInfos = mapping.getTypeInfos(); // Not needed anymore. vectorColumnMapping = null; diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnSourceMapping.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnSourceMapping.java index 4f5ba9a..061e396 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnSourceMapping.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorColumnSourceMapping.java @@ -19,6 +19,7 @@ package org.apache.hadoop.hive.ql.exec.vector; import org.apache.hadoop.hive.ql.exec.vector.VectorColumnOrderedMap.Mapping; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; /** * This class collects column information for copying a row from one VectorizedRowBatch to @@ -35,9 +36,9 @@ public VectorColumnSourceMapping(String name) { } @Override - public void add(int sourceColumn, int outputColumn, String typeName) { + public void add(int sourceColumn, int outputColumn, TypeInfo typeInfo) { // Order on sourceColumn. - vectorColumnMapping.add(sourceColumn, outputColumn, typeName); + vectorColumnMapping.add(sourceColumn, outputColumn, typeInfo); } @Override @@ -47,7 +48,7 @@ public void finalize() { // Ordered columns are the source columns. sourceColumns = mapping.getOrderedColumns(); outputColumns = mapping.getValueColumns(); - typeNames = mapping.getTypeNames(); + typeInfos = mapping.getTypeInfos(); // Not needed anymore. vectorColumnMapping = null; diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorCopyRow.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorCopyRow.java index c56903e..2f5bc29 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorCopyRow.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorCopyRow.java @@ -200,8 +200,7 @@ public void init(VectorColumnMapping columnMapping) throws HiveException { for (int i = 0; i < count; i++) { int inputColumn = columnMapping.getInputColumns()[i]; int outputColumn = columnMapping.getOutputColumns()[i]; - String typeName = columnMapping.getTypeNames()[i].toLowerCase(); - TypeInfo typeInfo = TypeInfoUtils.getTypeInfoFromTypeString(typeName); + TypeInfo typeInfo = columnMapping.getTypeInfos()[i]; Type columnVectorType = VectorizationContext.getColumnVectorTypeFromTypeInfo(typeInfo); CopyRow copyRowByValue = null; diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorDeserializeRow.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorDeserializeRow.java index 4d86db6..2c16d3d 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorDeserializeRow.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorDeserializeRow.java @@ -342,6 +342,10 @@ void apply(VectorizedRowBatch batch, int batchIndex) throws IOException { VectorizedBatchUtil.setNullColIsNullValue(colVector, batchIndex); } else { deserializeRead.readString(readStringResults); + + LOG.info("StringReaderByValue bytes null " + (readStringResults.bytes == null) + + " start " + readStringResults.start + " length " + readStringResults.length); + colVector.setVal(batchIndex, readStringResults.bytes, readStringResults.start, readStringResults.length); } @@ -684,7 +688,7 @@ public void deserializeByValue(VectorizedRowBatch batch, int batchIndex) throws readersByValue[i].apply(batch, batchIndex); i++; // Increment after the apply which could throw an exception. } - } catch (EOFException e) { + } catch (Exception e) { throwMoreDetailedException(e, i); } deserializeRead.extraFieldsCheck(); @@ -697,13 +701,13 @@ public void deserializeByReference(VectorizedRowBatch batch, int batchIndex) thr readersByReference[i].apply(batch, batchIndex); i++; // Increment after the apply which could throw an exception. } - } catch (EOFException e) { + } catch (Exception e) { throwMoreDetailedException(e, i); } deserializeRead.extraFieldsCheck(); } - private void throwMoreDetailedException(IOException e, int index) throws EOFException { + private void throwMoreDetailedException(Exception e, int index) throws IOException { StringBuilder sb = new StringBuilder(); sb.append("Detail: \"" + e.toString() + "\" occured for field " + index + " of " + typeInfos.length + " fields ("); for (int i = 0; i < typeInfos.length; i++) { @@ -713,6 +717,8 @@ private void throwMoreDetailedException(IOException e, int index) throws EOFExce sb.append(((PrimitiveTypeInfo) typeInfos[i]).getPrimitiveCategory().name()); } sb.append(")"); - throw new EOFException(sb.toString()); + sb.append(", deserialize context: "); + sb.append(deserializeRead.getCurrentContext()); + throw new IOException(sb.toString()); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorExtractRow.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorExtractRow.java index 4100bc5..a6b13d3 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorExtractRow.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorExtractRow.java @@ -22,6 +22,7 @@ import java.sql.Date; import java.sql.Timestamp; import java.util.List; + import org.apache.commons.lang.ArrayUtils; import org.apache.commons.lang.StringUtils; import org.slf4j.Logger; @@ -44,6 +45,7 @@ import org.apache.hadoop.hive.serde2.typeinfo.CharTypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.DecimalTypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils; import org.apache.hadoop.hive.serde2.typeinfo.VarcharTypeInfo; import org.apache.hadoop.io.BytesWritable; @@ -697,6 +699,18 @@ public void init(List typeNames) throws HiveException { } } + public void init(TypeInfo[] typeInfos) throws HiveException { + + extracters = new Extractor[typeInfos.length]; + + int i = 0; + for (TypeInfo typeInfo : typeInfos) { + PrimitiveTypeInfo primitiveTypeInfo = (PrimitiveTypeInfo) typeInfo; + extracters[i] = createExtractor(primitiveTypeInfo, i); + i++; + } + } + public int getCount() { return extracters.length; } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorGroupByOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorGroupByOperator.java index 0524c08..eca3a93 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorGroupByOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorGroupByOperator.java @@ -22,13 +22,10 @@ import java.lang.management.MemoryMXBean; import java.lang.ref.SoftReference; import java.util.ArrayList; -import java.util.Collection; import java.util.HashMap; import java.util.Iterator; import java.util.List; import java.util.Map; -import java.util.concurrent.Future; - import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.hadoop.conf.Configuration; @@ -347,7 +344,7 @@ public void processBatch(VectorizedRowBatch batch) throws HiveException { //Validate that some progress is being made if (!(numEntriesHashTable < preFlushEntriesCount)) { - if (LOG.isDebugEnabled()) { + if (isLogDebugEnabled) { LOG.debug(String.format("Flush did not progress: %d entries before, %d entries after", preFlushEntriesCount, numEntriesHashTable)); @@ -426,7 +423,7 @@ private void computeMemoryLimits() { maxHashTblMemory = (int)(maxMemory * memoryThreshold); - if (LOG.isDebugEnabled()) { + if (isLogDebugEnabled) { LOG.debug(String.format("maxMemory:%dMb (%d * %f) fixSize:%d (key:%d agg:%d)", maxHashTblMemory/1024/1024, maxMemory/1024/1024, @@ -449,7 +446,7 @@ private void flush(boolean all) throws HiveException { (int)(numEntriesHashTable * this.percentEntriesToFlush); int entriesFlushed = 0; - if (LOG.isDebugEnabled()) { + if (isLogDebugEnabled) { LOG.debug(String.format( "Flush %d %s entries:%d fixed:%d variable:%d (used:%dMb max:%dMb) gcCanary:%s", entriesToFlush, all ? "(all)" : "", @@ -482,7 +479,7 @@ private void flush(boolean all) throws HiveException { numEntriesHashTable = 0; } - if (all && LOG.isDebugEnabled()) { + if (all && isLogDebugEnabled) { LOG.debug(String.format("GC canary caused %d flushes", gcCanaryFlushes)); } } @@ -534,7 +531,7 @@ private void updateAvgVariableSize(VectorizedRowBatch batch) { private void checkHashModeEfficiency() throws HiveException { if (lastModeCheckRowCount > numRowsCompareHashAggr) { lastModeCheckRowCount = 0; - if (LOG.isDebugEnabled()) { + if (isLogDebugEnabled) { LOG.debug(String.format("checkHashModeEfficiency: HT:%d RC:%d MIN:%d", numEntriesHashTable, sumBatchSize, (long)(sumBatchSize * minReductionHashAggr))); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorMapJoinOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorMapJoinOperator.java index 8bbf020..5572a5c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorMapJoinOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorMapJoinOperator.java @@ -18,6 +18,7 @@ package org.apache.hadoop.hive.ql.exec.vector; +import java.io.IOException; import java.util.ArrayList; import java.util.Collection; import java.util.List; @@ -31,6 +32,7 @@ import org.apache.hadoop.hive.ql.exec.JoinUtil; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult.MapJoinResult; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriter; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpressionWriterFactory; @@ -178,8 +180,8 @@ protected Object _evaluate(Object row, int version) throws HiveException { } @Override - protected JoinUtil.JoinResult setMapJoinKey(ReusableGetAdaptor dest, Object row, byte alias) - throws HiveException { + protected MapJoinResult setMapJoinKey(ReusableGetAdaptor dest, Object row, byte alias) + throws HiveException, IOException { return dest.setFromVector(keyValues[batchIndex], keyOutputWriters, keyWrapperBatch); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizationContext.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizationContext.java index 95a4b9d..7c4a339 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizationContext.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizationContext.java @@ -160,7 +160,7 @@ public VectorizationContext(String contextName, List initialColumnNames) this.contextName = contextName; level = 0; if (LOG.isDebugEnabled()) { - LOG.debug("VectorizationContext consructor contextName " + contextName + " level " + LOG.debug("VectorizationContext constructor contextName " + contextName + " level " + level + " initialColumnNames " + initialColumnNames); } this.initialColumnNames = initialColumnNames; @@ -184,7 +184,7 @@ public VectorizationContext(String contextName) { this.contextName = contextName; level = 0; if (LOG.isDebugEnabled()) { - LOG.debug("VectorizationContext consructor contextName " + contextName + " level " + level); + LOG.debug("VectorizationContext constructor contextName " + contextName + " level " + level); } initialColumnNames = new ArrayList(); projectedColumns = new ArrayList(); @@ -410,7 +410,7 @@ private VectorExpression getColumnVectorExpression(ExprNodeColumnDesc expr = new SelectColumnIsTrue(columnNum); break; case PROJECTION: - expr = new IdentityExpression(columnNum, exprDesc.getTypeString()); + expr = new IdentityExpression(columnNum, exprDesc.getColumn(), exprDesc.getTypeString()); break; } return expr; @@ -963,20 +963,23 @@ private VectorExpression getIdentityExpression(List childExprList) throws HiveException { ExprNodeDesc childExpr = childExprList.get(0); int inputCol; + String name; String colType; VectorExpression v1 = null; if (childExpr instanceof ExprNodeGenericFuncDesc) { + name = ((ExprNodeGenericFuncDesc) childExpr).getName(); v1 = getVectorExpression(childExpr); inputCol = v1.getOutputColumn(); colType = v1.getOutputType(); } else if (childExpr instanceof ExprNodeColumnDesc) { + name = ((ExprNodeColumnDesc) childExpr) .getColumn(); ExprNodeColumnDesc colDesc = (ExprNodeColumnDesc) childExpr; inputCol = getInputColumnIndex(colDesc.getColumn()); colType = colDesc.getTypeString(); } else { throw new HiveException("Expression not supported: "+childExpr); } - VectorExpression expr = new IdentityExpression(inputCol, colType); + VectorExpression expr = new IdentityExpression(inputCol, name, colType); if (v1 != null) { expr.setChildExpressions(new VectorExpression [] {v1}); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizedBatchUtil.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizedBatchUtil.java index d75d185..1bf9782 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizedBatchUtil.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/VectorizedBatchUtil.java @@ -571,7 +571,7 @@ public static StandardStructObjectInspector convertToStandardStructObjectInspect } static ColumnVector cloneColumnVector(ColumnVector source - ) throws HiveException{ + ) throws HiveException { if (source instanceof LongColumnVector) { return new LongColumnVector(((LongColumnVector) source).vector.length); } else if (source instanceof DoubleColumnVector) { @@ -612,6 +612,17 @@ static ColumnVector cloneColumnVector(ColumnVector source " is not supported!"); } + public static PrimitiveTypeInfo[] primitiveTypeInfosFromTypeInfos( + TypeInfo[] typeInfos) throws HiveException { + + PrimitiveTypeInfo[] result = new PrimitiveTypeInfo[typeInfos.length]; + + for(int i = 0; i < typeInfos.length; i++) { + result[i] = (PrimitiveTypeInfo) typeInfos[i]; + } + return result; + } + /** * Make a new (scratch) batch, which is exactly "like" the batch provided, except that it's empty * @param batch the batch to imitate @@ -649,10 +660,16 @@ public static String displayBytes(byte[] bytes, int start, int length) { public static void debugDisplayOneRow(VectorizedRowBatch batch, int index, String prefix) { StringBuilder sb = new StringBuilder(); sb.append(prefix + " row " + index + " "); - for (int column = 0; column < batch.cols.length; column++) { + for (int p = 0; p < batch.projectionSize; p++) { + int column = batch.projectedColumns[p]; + if (p == column) { + sb.append("(col " + p + ") "); + } else { + sb.append("(proj col " + p + " col " + column + ") "); + } ColumnVector colVector = batch.cols[column]; if (colVector == null) { - sb.append("(null colVector " + column + ")"); + sb.append("(null ColumnVector)"); } else { boolean isRepeating = colVector.isRepeating; index = (isRepeating ? 0 : index); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/IdentityExpression.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/IdentityExpression.java index 402d0f8..df31c28 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/IdentityExpression.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/IdentityExpression.java @@ -28,13 +28,15 @@ private static final long serialVersionUID = 1L; private int colNum = -1; + private String name = null; private String type = null; public IdentityExpression() { } - public IdentityExpression(int colNum, String type) { + public IdentityExpression(int colNum, String name, String type) { this.colNum = colNum; + this.name = name; this.type = type; } @@ -68,6 +70,10 @@ public int getColNum() { return getOutputColumn(); } + public String getName() { + return name; + } + public String getType() { return getOutputType(); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommon.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommon.java new file mode 100644 index 0000000..7bdc881 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommon.java @@ -0,0 +1,504 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.util.ArrayList; +import java.util.HashMap; +import java.util.Map; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.conf.HiveConf; +import org.apache.hadoop.hive.ql.exec.Operator; +import org.apache.hadoop.hive.ql.exec.Utilities; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector.Type; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContextRegion; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr.BasicAggregation; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.BaseWork; +import org.apache.hadoop.hive.ql.plan.GroupByDesc; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc.HashTableKeyType; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunction; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.ColumnAggregationInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunctionInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc; +import org.apache.hadoop.hive.ql.plan.api.OperatorType; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen.VectorGroupByColAggrLongMap; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.gen.VectorGroupByColAggrDoubleMap; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/** + * This class is common operator class for native vectorized group by for initialization logic. + */ +public abstract class VectorGroupByCommon extends Operator implements + VectorizationContextRegion { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommon.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected VectorGroupByDesc vectorDesc; + + protected VectorGroupByInfo vectorGroupByInfo; + + protected boolean isVectorOutput; + + protected VectorizationContext vContext; + + /** + * Group by key vector expressions. + */ + protected VectorExpression[] groupByKeyExpressions; + + protected int[] aggregationFunctionInputColumns; + + protected TypeInfo[] aggregationFunctionInputTypeInfos; + + protected VectorExpression[] aggregationFunctionInputExpressions; + + protected TypeInfo[] aggregationFunctionOutputTypeInfos; + + // Create a new outgoing vectorization context because column name map will change. + protected VectorizationContext vOutContext; + + + // This is map of which vectorized row batch columns are the key columns. + protected int[] groupByKeyColumnMap; + protected TypeInfo[] groupByKeyTypeInfos; + protected Type[] groupByKeyColumnVectorTypes; + + // *** LONG COLUMN AGGREGATION MAP (does not include complex aggregation like AVG) *** + public static final Map longAggrFuncToBasicAggrMap = + new HashMap(); + static { + longAggrFuncToBasicAggrMap.put(AggregationFunction.MIN_FUNC, BasicAggregation.LONG_MIN); + longAggrFuncToBasicAggrMap.put(AggregationFunction.MAX_FUNC, BasicAggregation.LONG_MAX); + longAggrFuncToBasicAggrMap.put(AggregationFunction.COUNT_FUNC, BasicAggregation.COUNT); + longAggrFuncToBasicAggrMap.put(AggregationFunction.SUM_FUNC, BasicAggregation.LONG_SUM); + } + + // *** DOUBLE COLUMN AGGREGATION MAP (does not include complex aggregation like AVG) *** + public static final Map doubleAggrFuncToBasicAggrMap = + new HashMap(); + static { + doubleAggrFuncToBasicAggrMap.put(AggregationFunction.MIN_FUNC, BasicAggregation.DOUBLE_MIN); + doubleAggrFuncToBasicAggrMap.put(AggregationFunction.MAX_FUNC, BasicAggregation.DOUBLE_MAX); + doubleAggrFuncToBasicAggrMap.put(AggregationFunction.COUNT_FUNC, BasicAggregation.COUNT); + doubleAggrFuncToBasicAggrMap.put(AggregationFunction.SUM_FUNC, BasicAggregation.DOUBLE_SUM); + } + + // Is the group by only have one output for LONG type? COUNT(*). If so, we have a specialized + // operator for it. + protected boolean isOnlyCountStarLong; + + // Are we keeping the COUNT(*) counter for this group by. Note that sometimes we keep the + // counter for easy key aggregations even when COUNT(*) doesn't explicitly appear in the + // aggregation function outputs. + protected boolean hasCountStar; + + protected int countStarLongOffset; + + // The size of a key's entry in the longs and doubles arrays. + protected int entryLongLength; + + protected boolean keysAreAllFixedLength; + + protected int hasValuesFlagsLongOffset; + + // The offset into the key's longs entry of the first long of the key. + protected int keyBaseLongOffset; + + // The offset into the key's longs entry of the beginning of the aggregate(s). + protected int aggregationBaseLongOffset; + + + // Information on the Non-Key column aggregations we generate during constructor time for + // runtime usage. + protected NonKeyColumnAggregationInfo[] nonKeyColumnAggregationInfos; + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The maximum number of keys we'll keep in the hash table before flushing. + protected transient int keyLimit; + + // The logical size and power of 2 mask of the hash table + protected transient int logicalHashBucketCount; + protected transient int logicalHashBucketMask; + + // For debug tracing: the name of the map or reduce task. + protected transient String taskName; + + // Debug display. + protected transient long batchCounter; + + //--------------------------------------------------------------------------- + + // **** LONG NON-KEY ***** + protected static Map>> longColAggrMap = + new HashMap>>(); + static { + + VectorGroupByColAggrLongMap.fillMap(longColAggrMap); + + } + + //--------------------------------------------------------------------------- + + // **** DOUBLE NON-KEY ***** + protected static Map>> doubleColAggrMap = + new HashMap>>(); + static { + + VectorGroupByColAggrDoubleMap.fillMap(doubleColAggrMap); + + } + + //--------------------------------------------------------------------------- + + public VectorGroupByCommon() { + super(); + } + + public static int INT_PER_LONG_COUNT = Long.SIZE / Integer.SIZE; + + public VectorGroupByCommon(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(); + + GroupByDesc desc = (GroupByDesc) conf; + this.conf = desc; + vectorDesc = desc.getVectorDesc(); + vectorGroupByInfo = vectorDesc.getVectorGroupByInfo(); + isVectorOutput = vectorDesc.isVectorOutput(); + this.vContext = vContext; + + // Since a key expression can be a calculation and the key will go into a scratch column, + // we need the mapping and type information. + groupByKeyColumnMap = vectorGroupByInfo.getGroupByKeyColumnMap(); + groupByKeyTypeInfos = vectorGroupByInfo.getGroupByKeyTypeInfos(); + groupByKeyColumnVectorTypes = vectorGroupByInfo.getGroupByKeyColumnVectorTypes(); + groupByKeyExpressions = vectorGroupByInfo.getGroupByKeyExpressions(); + + aggregationFunctionInputColumns = vectorGroupByInfo.getAggregationFunctionInputColumns(); + aggregationFunctionInputTypeInfos = vectorGroupByInfo.getAggregationFunctionInputTypeInfos(); + aggregationFunctionInputExpressions = vectorGroupByInfo.getAggregationFunctionInputExpressions(); + + aggregationFunctionOutputTypeInfos = vectorGroupByInfo.getAggregationFunctionOutputTypeInfos(); + + vOutContext = new VectorizationContext(getName(), desc.getOutputColumnNames()); + + entryLongLength = 0; + keysAreAllFixedLength = vectorGroupByInfo.getKeysAreAllFixedLength(); + + // For variable length entries, make first word the entry length. + switch (vectorDesc.hashTableKeyType()) { + case LONG: + break; + case STRING: + case MULTI_KEY: + entryLongLength++; // First long is the variable entry long length. + break; + default: + throw new RuntimeException("Unexpected groupby hash table key type " + + vectorDesc.hashTableKeyType().name()); + } + + boolean isOnlyCountStar = vectorGroupByInfo.getHasCountStar() && !vectorGroupByInfo.getHasNonCountStar(); + + isOnlyCountStarLong = + isOnlyCountStar && vectorDesc.hashTableKeyType() == HashTableKeyType.LONG; + + if (isOnlyCountStarLong) { + + // VectorGroupByLongCountStarHashTable optimizes by keeping COUNT(*) in hash table. + // The longs array is not used. + + hasCountStar = true; + + } else { + hasCountStar = vectorGroupByInfo.getHasCountStar(); + + // Add 1 for hasValuesFlagWord. + hasValuesFlagsLongOffset = entryLongLength++; + + countStarLongOffset = -1; // Assume. + + aggregationBaseLongOffset = entryLongLength; + + prepareAllInfoForRuntime(vectorGroupByInfo); + + keyBaseLongOffset = entryLongLength; + + // Allocate fixed length key. + switch (vectorDesc.hashTableKeyType()) { + case LONG: + entryLongLength++; + break; + case STRING: + case MULTI_KEY: + if (vectorGroupByInfo.getKeysAreAllFixedLength()) { + // NULL key mask + N long keys. + entryLongLength += 1 + groupByKeyColumnMap.length; + } + break; + default: + throw new RuntimeException("Unexpected groupby hash table key type " + + vectorDesc.hashTableKeyType().name()); + } + } + } + + /** + * Information on a Non-Key column aggregations we generate during constructor time for + * runtime usage. + * + * Adds the column number, column aggregate position (number), and the basic aggregate offsets. + */ + public class NonKeyColumnAggregationInfo { + + private final ColumnAggregationInfo columnAggregationInfo; + + private final int inputColumnNum; + + ArrayList> columnAggregationClasses; + + // Offset to long/double/etc aggregate. + private final int[] basicAggregationOffsets; + + public NonKeyColumnAggregationInfo(ColumnAggregationInfo columnAggregationInfo) { + this.columnAggregationInfo = columnAggregationInfo; + inputColumnNum = columnAggregationInfo.getInputColumnNum(); + columnAggregationClasses = null; + basicAggregationOffsets = new int[BasicAggregation.basicAggregationValues.length]; + } + + public ColumnAggregationInfo getColumnAggregationInfo() { + return columnAggregationInfo; + } + + public int getInputColumnNum() { + return inputColumnNum; + } + + public int[] getBasicAggregationOffsets() { + return basicAggregationOffsets; + } + + public ArrayList> getColumnAggregationClasses() { + return columnAggregationClasses; + } + public void setColumnAggregationClasses(ArrayList> columnAggregationClasses) { + this.columnAggregationClasses = columnAggregationClasses; + } + } + + /** + * Determine the rest of the constructor time information needed for runtime. + */ + private void prepareAllInfoForRuntime(VectorGroupByInfo vectorGroupByInfo) + throws HiveException { + + /** + * Build array of runtime objects for non-key columns (to hold the aggregation offsets). + */ + ArrayList nonKeyColumnAggrInfoList = + new ArrayList(); + + int nextLogicalNonKeyColumnNum = 0; + for (AggregationFunctionInfo aggrFuncInfo : vectorGroupByInfo.getAggregationFunctionInfos()) { + + ColumnAggregationInfo columnAggrInfo = aggrFuncInfo.getColumnAggregationInfo(); + + // NOTE: For COUNT(*), no columnAggrInfo. + if (columnAggrInfo != null && !columnAggrInfo.getIsKey() && + nextLogicalNonKeyColumnNum == columnAggrInfo.getLogicalNonKeyColumnNum()) { + + NonKeyColumnAggregationInfo nonKeyColumnAggrInfo = + new NonKeyColumnAggregationInfo(columnAggrInfo); + nonKeyColumnAggrInfoList.add(nonKeyColumnAggrInfo); + nextLogicalNonKeyColumnNum++; + } + } + + determineNonKeyColumnAggrClasses(nonKeyColumnAggrInfoList); + + setupVectorOutput(vectorGroupByInfo.getAggregationFunctionInfos(), nonKeyColumnAggrInfoList); + + nonKeyColumnAggregationInfos = + nonKeyColumnAggrInfoList.toArray(new NonKeyColumnAggregationInfo[0]); + + } + + /** + * Determine the object needed for each aggregation function output. + */ + protected abstract void setupVectorOutput(AggregationFunctionInfo[] aggrFuncInfos, + ArrayList nonKeyColumnAggrInfoList); + + /** + * Determines which aggregation class is needed for each Non-Key column. + * + * The aggregation class handles the combination of basic aggregations (MIN, MAX, + * COUNT, SUM, etc) needed for each column + */ + private void determineNonKeyColumnAggrClasses(ArrayList nonKeyColumnAggrInfoList) { + + int currentLongsOffset = 0; + + if (hasCountStar) { + countStarLongOffset = currentLongsOffset++; + } + + for (NonKeyColumnAggregationInfo nonKeyColumnAggrInfo : nonKeyColumnAggrInfoList) { + + ColumnAggregationInfo columnAggrInfo = nonKeyColumnAggrInfo.getColumnAggregationInfo(); + Preconditions.checkState(!columnAggrInfo.getIsKey()); + ColumnVector.Type inputColumnVectorType = columnAggrInfo.getInputColumnVectorType(); + + int[] basicAggregationOffsets = nonKeyColumnAggrInfo.getBasicAggregationOffsets(); + + boolean[] aggregationFunctionsPresent = columnAggrInfo.getAggregationFunctionsPresent(); + + ArrayList> columnAggregationClasses = null; + + switch (inputColumnVectorType) { + case LONG: + { + int mask = 0; + for (AggregationFunction aggrFunc : AggregationFunction.aggregationFunctionValues) { + if (aggregationFunctionsPresent[aggrFunc.getValue()]) { + + if (aggrFunc != AggregationFunction.AVG_FUNC) { + BasicAggregation basicAggr = longAggrFuncToBasicAggrMap.get(aggrFunc); + basicAggregationOffsets[basicAggr.value] = currentLongsOffset++; + mask |= basicAggr.mask; + } else { + basicAggregationOffsets[BasicAggregation.COUNT.value] = currentLongsOffset++; + mask |= BasicAggregation.COUNT.mask; + + basicAggregationOffsets[BasicAggregation.DOUBLE_SUM.value] = currentLongsOffset++; + mask |= BasicAggregation.DOUBLE_SUM.mask; + } + } + } + + columnAggregationClasses = longColAggrMap.get(mask); + } + break; + + case DOUBLE: + { + int mask = 0; + for (AggregationFunction aggrFunc : AggregationFunction.aggregationFunctionValues) { + if (aggregationFunctionsPresent[aggrFunc.getValue()]) { + + if (aggrFunc != AggregationFunction.AVG_FUNC) { + BasicAggregation basicAggr = doubleAggrFuncToBasicAggrMap.get(aggrFunc); + basicAggregationOffsets[basicAggr.value] = currentLongsOffset++; + mask |= basicAggr.mask; + } else { + basicAggregationOffsets[BasicAggregation.COUNT.value] = currentLongsOffset++; + mask |= BasicAggregation.COUNT.mask; + + basicAggregationOffsets[BasicAggregation.DOUBLE_SUM.value] = currentLongsOffset++; + mask |= BasicAggregation.DOUBLE_SUM.mask; + } + } + } + + columnAggregationClasses = doubleColAggrMap.get(mask); + } + break; + + default: + throw new RuntimeException( + "Vector column type " + inputColumnVectorType.name() + " not supported yet"); + } + + nonKeyColumnAggrInfo.setColumnAggregationClasses(columnAggregationClasses); + } + + // Save long final entry size. + entryLongLength = aggregationBaseLongOffset + currentLongsOffset; + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + if (isLogDebugEnabled) { + // Determine the name of our map or reduce task for debug tracing. + BaseWork work = Utilities.getMapWork(hconf); + if (work == null) { + work = Utilities.getReduceWork(hconf); + } + taskName = work.getName(); + } + + // UNDONE: This needs to have sanity checks, etc. + // UNDONE: And, a lot more work. + // UNDONE: How do we obtain the amount of available memory? + long availableMemory = 1L << 22; // 4 Mb. + + float groupByMemoryUsage = HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEMAPAGGRHASHMEMORY); + + long usableMemory = (long) (availableMemory * groupByMemoryUsage); + + // UNDONE: Give the slot table 8 time (* 2 since we use a pair of long) more than the key limit. + // UNDONE: We allocate the longs array based on key limit. + int factor = 8*2 + entryLongLength; + + // UNDONE: Missing sanity checks and limiting to int range. + keyLimit = (int) usableMemory / factor; + + // UNDONE: What factor here? + int rawHashBucketCount = 8 * keyLimit; + + // Must be a power of 2 so the mask works. + logicalHashBucketCount = 1 << (Long.SIZE - Long.numberOfLeadingZeros(rawHashBucketCount)); + logicalHashBucketMask = logicalHashBucketCount - 1; + + batchCounter = 0; + } + + static public String getOperatorName() { + return "GBY"; + } + + @Override + public VectorizationContext getOuputVectorizationContext() { + return vOutContext; + } + + @Override + public OperatorType getType() { + return OperatorType.GROUPBY; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonAggregation.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonAggregation.java new file mode 100644 index 0000000..cfbe2de --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonAggregation.java @@ -0,0 +1,206 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.util.ArrayList; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccessImpl; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr.BatchVariation; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc.HashTableKeyType; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * This class is common operator class for native vectorized group by for aggregation logic. + */ +public abstract class VectorGroupByCommonAggregation extends VectorGroupByCommonStorage { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonAggregation.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + protected transient boolean atLeastAllNullKey; + + protected transient VectorGroupByColAggr[][] nonKeyColAggrAllBatchVariants; + + protected transient VectorGroupByColAggr[] currentNonKeyColumnAggregations; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByCommonAggregation() { + super(); + } + + public VectorGroupByCommonAggregation(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + atLeastAllNullKey = false; + + if (isOnlyCountStarLong) { + + // VectorGroupByLongCountStarHashTable optimizes by keeping COUNT(*) in hash table. + // The longs array is not used. + + } else { + int size = nonKeyColumnAggregationInfos.length; + nonKeyColAggrAllBatchVariants = new VectorGroupByColAggr[size][]; + for (int i = 0; i < size; i++) { + NonKeyColumnAggregationInfo nonKeyColumnAggregationInfo = nonKeyColumnAggregationInfos[i]; + ArrayList> colAggrClasses = + nonKeyColumnAggregationInfo.getColumnAggregationClasses(); + int inputColumnNum = nonKeyColumnAggregationInfo.getInputColumnNum(); + + VectorGroupByColAggr[] nonKeyColAggrBatchVariants = + new VectorGroupByColAggr[colAggrClasses.size()]; + nonKeyColAggrAllBatchVariants[i] = nonKeyColAggrBatchVariants; + for (int j = 0; j < colAggrClasses.size(); j++) { + VectorGroupByColAggr colAggr; + try { + colAggr = + colAggrClasses.get(j).getDeclaredConstructor(int.class).newInstance(inputColumnNum); + } catch (Exception e) { + throw new HiveException(e); + } + colAggr.setMemoryRepresentation(aggrEntryAccess); + nonKeyColAggrBatchVariants[j] = colAggr; + } + } + + currentNonKeyColumnAggregations = new VectorGroupByColAggr[size]; + } + } + + protected void chooseNonKeyBatchVariants(VectorizedRowBatch batch) { + + if (!isOnlyCountStarLong) { + int size = nonKeyColumnAggregationInfos.length; + for (int i = 0; i < size; i++) { + NonKeyColumnAggregationInfo nonKeyColumnAggregationInfo = nonKeyColumnAggregationInfos[i]; + int inputColumnNum = nonKeyColumnAggregationInfo.getInputColumnNum(); + VectorGroupByColAggr[] nonKeyColAggrBatchVariants = nonKeyColAggrAllBatchVariants[i]; + + ColumnVector colVector = batch.cols[inputColumnNum]; + BatchVariation batchVariation; + if (colVector.isRepeating) { + if (colVector.noNulls){ + batchVariation = BatchVariation.REPEATING_NONULLS; + } else { + batchVariation = BatchVariation.REPEATING_NULLS; + } + } else if (batch.selectedInUse) { + if (colVector.noNulls){ + batchVariation = BatchVariation.SELECTED_NONULLS; + } else { + batchVariation = BatchVariation.SELECTED_NULLS; + } + } else { + if (colVector.noNulls){ + batchVariation = BatchVariation.NONSELECTED_NONULLS; + } else { + batchVariation = BatchVariation.NONSELECTED_NULLS; + } + } + VectorGroupByColAggr colAggrVariant = nonKeyColAggrBatchVariants[batchVariation.index]; + colAggrVariant.setColumnVectors(batch); + currentNonKeyColumnAggregations[i] = colAggrVariant; + } + } + } + + protected void forgetNonKeyBatchVariants() { + if (!isOnlyCountStarLong) { + int size = nonKeyColumnAggregationInfos.length; + for (int i = 0; i < size; i++) { + VectorGroupByColAggr colAggrVariant = currentNonKeyColumnAggregations[i]; + colAggrVariant.forgetColumnVectors(); + } + } + } + + /** + * Fill in the aggregates for the non-key columns (unless a row's column value is NULL). + * + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param entryBaseLongIndex The index into longs of the long entry. + */ + protected void aggregate(int startIndex, int duplicateCount, int entryBaseLongIndex) { + + int colBaseAggrLongIndex = entryBaseLongIndex + aggregationBaseLongOffset; + + if (hasCountStar) { + + // COUNT(*) includes NULLs. + aggrEntryAccess.incrCountStar(entryBaseLongIndex, duplicateCount); + colBaseAggrLongIndex++; + } + + final int size = currentNonKeyColumnAggregations.length; + if (size == 0) { + // Only COUNT(*). + return; + } + + long hasValuesBitFlags = aggrEntryAccess.getHasValuesBitFlags(entryBaseLongIndex); + + long colAggrHasValueBitFlag; + boolean hasValues; + boolean newHasValues; + + int logicalNonKeyColumnNum = 0; + VectorGroupByColAggr colAggr = currentNonKeyColumnAggregations[0]; + do { + colAggrHasValueBitFlag = 1L << logicalNonKeyColumnNum; + hasValues = (hasValuesBitFlags & colAggrHasValueBitFlag) != 0; + newHasValues = colAggr.aggregateColumnAggregation(startIndex, duplicateCount, hasValues, + colBaseAggrLongIndex); + if (!hasValues && newHasValues) { + aggrEntryAccess.mergeHasValuesBitFlags(entryBaseLongIndex, colAggrHasValueBitFlag); + } + + if (++logicalNonKeyColumnNum >= size) { + return; + } + + // Skip over the aggregates for the column. + colBaseAggrLongIndex += colAggr.getAggrCount(); + + colAggr = currentNonKeyColumnAggregations[logicalNonKeyColumnNum]; + } while (true); + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonHashTable.java new file mode 100644 index 0000000..eb8d2f9 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonHashTable.java @@ -0,0 +1,109 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * This class is common operator class for native vectorized group by for hash table logic. + */ +public abstract class VectorGroupByCommonHashTable extends VectorGroupByCommonOutput { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonHashTable.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The find or create results for findOrCreateLongKey and findOrCreateNonLongKey. + + public transient boolean currentIsNewKey; + public transient int currentLongIndex; + public transient int currentDoublesIndex; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByCommonHashTable() { + super(); + } + + public VectorGroupByCommonHashTable(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + } + + protected void clearHashTable() { + Arrays.fill(slotMultiples, 0, slotPhysicalArraySize, 0); + keyCount = 0; + largestNumberOfSteps = 0; + metricPutConflict = 0; + } + + public void outputAggregationsAndClearHashTable() throws HiveException, IOException { + outputGroupBy(/* flushAllNullKey */ false); + if (outputBatch.size > 0) { + forwardOutputBatch(outputBatch); + } + + clearHashTable(); + + // Don't clear all NULL entry. + clearStorage(/* startIndex */ entryLongLength); + } + + /* + * The hash table slots. For a long key hash table, each slot is 2 longs and the array is + * 2X sized. + * + * The slot pair is 1) a non-zero storage word to the first value bytes and + * 2) the long value or hash code. + */ + int slotPhysicalArraySize; + protected long[] slotMultiples; + + int keyCount; + int largestNumberOfSteps; + int metricPutConflict; + + protected void allocateBucketArray(int multiplier) { + slotPhysicalArraySize = multiplier * logicalHashBucketCount; + slotMultiples = new long[slotPhysicalArraySize]; + + keyCount = 0; + largestNumberOfSteps = 0; + metricPutConflict = 0; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonOutput.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonOutput.java new file mode 100644 index 0000000..036de46 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonOutput.java @@ -0,0 +1,1192 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorExtractRowSameBatch; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatchCtx; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByColAggr.BasicAggregation; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc.HashTableKeyType; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunction; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunctionInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.ColumnAggregationInfo; +import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector; +import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory; +import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils; +import org.apache.hadoop.hive.serde2.objectinspector.StandardStructObjectInspector; +import org.apache.hadoop.hive.serde2.objectinspector.StructField; +import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/* + * Specialized class for doing a vectorized group by that is lookup on multiple key columns (or a + * single non-long column) using a hash map. + */ +public abstract class VectorGroupByCommonOutput extends VectorGroupByCommonAggregation { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonOutput.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + protected transient VectorizedRowBatch outputBatch; + + private transient VectorizedRowBatchCtx vrbCtx; + + private transient TypeInfo[] outputTypes; + + private transient VectorExtractRowSameBatch vectorExtractRowSameBatch; + + private transient Object[] singleRow; + + private transient StandardStructObjectInspector standardOutputObjInspector; + + /** + * This class is common operator class for native vectorized group by for output logic. + */ + private static abstract class VectorOutputAggr { + + private static final long serialVersionUID = 1L; + + protected final int aggrOutColumnNum; + + protected ColumnVector aggrOutColVector; + + public VectorOutputAggr(int aggrOutColumnNum) { + this.aggrOutColumnNum = aggrOutColumnNum; + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + aggrOutColVector = outputBatch.cols[aggrOutColumnNum]; + } + + /** + * Populate the aggregation function output column with one or more basic aggregations. + * @param aggrEntryAccess Used to access things in the hash table entry. + * @param entryBaseLongIndex Index into the longs array of the beginning of the hase table entry. + * @param startBatchIndex The batch index of the first row. + * @param count The number of rows to process the aggregation on. + */ + public abstract void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, + int startBatchIndex, int count); + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(" aggr output column "); + sb.append(aggrOutColumnNum); + return sb.toString(); + } + } + + /** + * Handles populating an aggregation function COUNT(*) output column. + */ + private static class VectorOutputCountStarAggr extends VectorOutputAggr { + + protected long[] aggrOutVector; + + public VectorOutputCountStarAggr(int aggrOutColumnNum) { + super(aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputCountStarAggr(aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + aggrOutVector = ((LongColumnVector) aggrOutColVector).vector; + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, + int count) { + + while (true) { + aggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex); + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" COUNT(*) "); + return sb.toString(); + } + } + + /** + * Base abstract class for populating one column of the output for an aggregation function + * on a key. + */ + private static abstract class VectorOutputKeyAggr extends VectorOutputAggr { + + private static final long serialVersionUID = 1L; + + protected final int keyOutColumnNum; + + // When the apply function is called, the output key column(s) have been populated. + protected ColumnVector keyOutColVector; + + protected ColumnVector aggrOutColVector; + + public VectorOutputKeyAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(aggrOutColumnNum); + this.keyOutColumnNum = keyOutColumnNum; + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + keyOutColVector = outputBatch.cols[keyOutColumnNum]; + aggrOutColVector = outputBatch.cols[aggrOutColumnNum]; + } + + /** + * Populate the aggregation function output column with one or more aggregations. + */ + public abstract void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, + int startBatchIndex, int count); + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append("key output column "); + sb.append(keyOutColumnNum); + sb.append(" aggr output column "); + sb.append(aggrOutColumnNum); + return sb.toString(); + } + } + + // Key Count: Easy. Single-Key case (null checking not required) + private static class VectorOutputCountKeySingleAggr extends VectorOutputKeyAggr { + + protected long[] aggrOutVector; + + public VectorOutputCountKeySingleAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputCountKeySingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + aggrOutVector = ((LongColumnVector) aggrOutColVector).vector; + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, + int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeyCountSingle keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + // Check the ALL NULLs entry. + if (entryBaseLongIndex == 0) { + Preconditions.checkState(count == 1); + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + return; + } + + while (true) { + + // COUNT(key) uses implicit COUNT(*). + aggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex); + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" COUNT(key) (NULL checking not required for Single-Key)"); + return sb.toString(); + } + } + + // Key Count: Easy. Multi-Key case (null checking required). + private static class VectorOutputCountKeyMultiAggr extends VectorOutputKeyAggr { + + protected long[] aggrOutVector; + + public VectorOutputCountKeyMultiAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputCountKeyMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + aggrOutVector = ((LongColumnVector) aggrOutColVector).vector; + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeyCountMulti keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if (keyOutColVector.noNulls || !keyOutColVector.isNull[batchIndex]) { + + // COUNT(key) uses implicit COUNT(*). + aggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex); + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" COUNT(key) (NULL checking required for Multi-Key)"); + return sb.toString(); + } + } + + private static abstract class VectorOutputLongKeyAggr extends VectorOutputKeyAggr { + + private static final long serialVersionUID = 1L; + + // When the apply function is called, the output key column(s) have been populated. + protected long[] keyOutVector; + + protected long[] aggrOutVector; + + public VectorOutputLongKeyAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + keyOutVector = ((LongColumnVector) keyOutColVector).vector; + aggrOutVector = ((LongColumnVector) aggrOutColVector).vector; + } + } + + + // Key trivial: MIN and MAX. Single-Key case (check if it is all NULLs entry). + private static class VectorOutputLongKeyTrivialSingleAggr extends VectorOutputLongKeyAggr { + + public VectorOutputLongKeyTrivialSingleAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongKeyTrivialSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeyTrivialSingle keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + // Check the ALL NULLs entry. + if (entryBaseLongIndex == 0) { + Preconditions.checkState(count == 1); + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + return; + } + + while (true) { + + // MIN and MAX are just the key value itself. + aggrOutVector[batchIndex] = keyOutVector[batchIndex]; + if (--count <= 0) { + break; + } + batchIndex++; + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" MIN(key) or MAX(key) (NULL checking not required for Single-Key)"); + return sb.toString(); + } + } + + // Key trivial: MIN and MAX. Multi-Key case (null checking required). + private static class VectorOutputLongKeyTrivialMultiAggr extends VectorOutputLongKeyAggr { + + public VectorOutputLongKeyTrivialMultiAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongKeyTrivialMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeyTrivialMulti keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if (keyOutColVector.noNulls || !keyOutColVector.isNull[batchIndex]) { + + // MIN and MAX are just the key value itself. + aggrOutVector[batchIndex] = keyOutVector[batchIndex]; + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" MIN(key) or MAX(key) (NULL checking required for Multi-Key)"); + return sb.toString(); + } + } + + + + // Key Sum: Easy. Single-Key case (null checking not required). + private static class VectorOutputLongKeySumSingleAggr extends VectorOutputLongKeyAggr { + + public VectorOutputLongKeySumSingleAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongKeySumSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeySumSingle keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + // Check the ALL NULLs entry. + if (entryBaseLongIndex == 0) { + Preconditions.checkState(count == 1); + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + return; + } + + while (true) { + // Key SUM uses implicit COUNT(*). + aggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex) * keyOutVector[batchIndex]; + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" Key Sum (NULL checking not required for Single-Key)"); + return sb.toString(); + } + } + + // Key Sum: Easy. Multi-Key case (null checking required). + private static class VectorOutputLongKeySumMultiAggr extends VectorOutputLongKeyAggr { + + public VectorOutputLongKeySumMultiAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongKeySumMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongKeySumMulti keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if (keyOutColVector.noNulls || !keyOutColVector.isNull[batchIndex]) { + + // Key SUM uses implicit COUNT(*). + aggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex) * keyOutVector[batchIndex]; + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" Key Sum (NULL checking required for Multi-Key)"); + return sb.toString(); + } + } + + private static abstract class VectorOutputDoubleKeyAggr extends VectorOutputKeyAggr { + + private static final long serialVersionUID = 1L; + + // When the apply function is called, the output key column(s) have been populated. + protected double[] doubleKeyOutVector; + + protected double[] doubleAggrOutVector; + + public VectorOutputDoubleKeyAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + doubleKeyOutVector = ((DoubleColumnVector) keyOutColVector).vector; + doubleAggrOutVector = ((DoubleColumnVector) aggrOutColVector).vector; + } + } + + + // Key trivial: MIN and MAX. Single-Key case (check if it is all NULLs entry). + private static class VectorOutputDoubleKeyTrivialSingleAggr extends VectorOutputDoubleKeyAggr { + + public VectorOutputDoubleKeyTrivialSingleAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputDoubleKeyTrivialSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrDoubleKeyTrivialSingle keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + // Check the ALL NULLs entry. + if (entryBaseLongIndex == 0) { + Preconditions.checkState(count == 1); + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + return; + } + + while (true) { + + // MIN and MAX are just the key value itself. + doubleAggrOutVector[batchIndex] = doubleKeyOutVector[batchIndex]; + if (--count <= 0) { + break; + } + batchIndex++; + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" MIN(key) or MAX(key) (NULL checking not required for Single-Key)"); + return sb.toString(); + } + } + + // Key trivial: MIN and MAX. Multi-Key case (null checking required). + private static class VectorOutputDoubleKeyTrivialMultiAggr extends VectorOutputDoubleKeyAggr { + + public VectorOutputDoubleKeyTrivialMultiAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputDoubleKeyTrivialMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrDoubleKeyTrivialMulti keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if (keyOutColVector.noNulls || !keyOutColVector.isNull[batchIndex]) { + + // MIN and MAX are just the key value itself. + doubleAggrOutVector[batchIndex] = doubleKeyOutVector[batchIndex]; + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" MIN(key) or MAX(key) (NULL checking required for Multi-Key)"); + return sb.toString(); + } + } + + + + // Key Sum: Easy. Single-Key case (null checking not required). + private static class VectorOutputDoubleKeySumSingleAggr extends VectorOutputDoubleKeyAggr { + + public VectorOutputDoubleKeySumSingleAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputDoubleKeySumSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrDoubleKeySumSingle keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + // Check the ALL NULLs entry. + if (entryBaseLongIndex == 0) { + Preconditions.checkState(count == 1); + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + return; + } + + while (true) { + // Key SUM uses implicit COUNT(*). + doubleAggrOutVector[batchIndex] = + aggrEntryAccess.getCountStar(entryBaseLongIndex) * doubleKeyOutVector[batchIndex]; + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" Key Sum (NULL checking not required for Single-Key)"); + return sb.toString(); + } + } + + // Key Sum: Easy. Multi-Key case (null checking required). + private static class VectorOutputDoubleKeySumMultiAggr extends VectorOutputDoubleKeyAggr { + + public VectorOutputDoubleKeySumMultiAggr(int keyOutColumnNum, int aggrOutColumnNum) { + super(keyOutColumnNum, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputDoubleKeySumMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrDoubleKeySumMulti keyOutColumnNum " + keyOutColumnNum + + // " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if (keyOutColVector.noNulls || !keyOutColVector.isNull[batchIndex]) { + + // Key SUM uses implicit COUNT(*). + doubleAggrOutVector[batchIndex] = aggrEntryAccess.getCountStar(entryBaseLongIndex) * doubleKeyOutVector[batchIndex]; + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" Key Sum (NULL checking required for Multi-Key)"); + return sb.toString(); + } + } + + /** + * Populate an aggregation function output column for a Non-Key. + * + * Copies the aggregate by offset to the output when there are values for the column aggregates. + */ + private static abstract class VectorOutputNonKeyAggr extends VectorOutputAggr { + + // The column position needed for has values flag testing. + protected final int logicalNonKeyColumnNum; + + // The offset of the long aggregate. + protected final int aggregateOffset; + + // The offset of the has values flag word. + protected final int hasValuesOffset; + + // The has values mask computed from the column position. + protected final long hasValuesMask; + + public VectorOutputNonKeyAggr(int logicalNonKeyColumnNum, int aggregateOffset, + int hasValuesOffset, int aggrOutColumnNum) { + super(aggrOutColumnNum); + this.logicalNonKeyColumnNum = logicalNonKeyColumnNum; + this.aggregateOffset = aggregateOffset; + this.hasValuesOffset = hasValuesOffset; + + hasValuesMask = (1L << logicalNonKeyColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongNonKeyAggr(logicalNonKeyColumnNum, aggregateOffset, + hasValuesOffset, aggrOutColumnNum); + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append(super.toString()); + sb.append(" NonKey logicalNonKeyColumnNum "); + sb.append(logicalNonKeyColumnNum); + sb.append(" aggregation offset "); + sb.append(aggregateOffset); + return sb.toString(); + } + } + + /** + * Populate an aggregation function output column for a long Non-Key. + * + * Copies the aggregate by offset to the output when there are values for the column aggregates. + */ + private static class VectorOutputLongNonKeyAggr extends VectorOutputNonKeyAggr { + + protected long[] aggrOutVector; + + public VectorOutputLongNonKeyAggr(int logicalNonKeyColumnNum, int aggregateOffset, + int hasValuesOffset, int aggrOutColumnNum) { + super(logicalNonKeyColumnNum, aggregateOffset, hasValuesOffset, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputLongNonKeyAggr(logicalNonKeyColumnNum, aggregateOffset, + hasValuesOffset, aggrOutColumnNum); + } + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + aggrOutVector = ((LongColumnVector) aggrOutColVector).vector; + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrLongNonKey logicalNonKeyColumnNum " + + // logicalNonKeyColumnNum + " aggregateOffset " + aggregateOffset + " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if ((aggrEntryAccess.getHasValuesBitFlags(entryBaseLongIndex) & hasValuesMask) != 0) { + aggrOutVector[batchIndex] = + aggrEntryAccess.getLongAggregationValue(entryBaseLongIndex, aggregateOffset); + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + } + + /** + * Populate an aggregation function output column for a double Non-Key. + * + * Copies the aggregate by offset to the output when there are values for the column aggregates. + */ + private static class VectorOutputDoubleNonKeyAggr extends VectorOutputNonKeyAggr { + + protected double[] doubleAggrOutVector; + + public VectorOutputDoubleNonKeyAggr(int logicalNonKeyColumnNum, int aggregateOffset, + int hasValuesOffset, int aggrOutColumnNum) { + super(logicalNonKeyColumnNum, aggregateOffset, hasValuesOffset, aggrOutColumnNum); + } + + @Override + protected Object clone() { + return new VectorOutputDoubleNonKeyAggr(logicalNonKeyColumnNum, aggregateOffset, + hasValuesOffset, aggrOutColumnNum); + } + + static int fake = 0; + + /** + * Set the output batch once during runtime. + */ + public void setBatch(VectorizedRowBatch outputBatch) { + super.setBatch(outputBatch); + if (!(aggrOutColVector instanceof DoubleColumnVector)) { + fake++; + } + doubleAggrOutVector = ((DoubleColumnVector) aggrOutColVector).vector; + } + + public void apply(VectorGroupByAggrEntryAccess aggrEntryAccess, int entryBaseLongIndex, int batchIndex, int count) { + + // LOG.info("setupVectorOutput VectorOutputAggrDoubleNonKey logicalNonKeyColumnNum " + + // logicalNonKeyColumnNum + " aggregateOffset " + aggregateOffset + " aggrOutColumnNum " + aggrOutColumnNum); + + while (true) { + if ((aggrEntryAccess.getHasValuesBitFlags(entryBaseLongIndex) & hasValuesMask) != 0) { + doubleAggrOutVector[batchIndex] = + aggrEntryAccess.getDoubleAggregationValue(entryBaseLongIndex, aggregateOffset); + } else { + aggrOutColVector.noNulls = false; + aggrOutColVector.isNull[batchIndex] = true; + } + if (--count <= 0) { + break; + } + batchIndex++; + entryBaseLongIndex = aggrEntryAccess.nextEntryIndex(entryBaseLongIndex); + } + } + } + + /** + * Determines which aggregation class is needed for each Non-Key column. + * + * The aggregation class handles the combination of basic aggregations (MIN, MAX, + * COUNT, SUM, etc) needed for each column + */ + @Override + protected void setupVectorOutput( + AggregationFunctionInfo[] aggrFuncInfos, + ArrayList nonKeyColumnAggrInfoList) { + + ArrayList vectorOutputAggrList = new ArrayList(); + + boolean isMultiKey = (groupByKeyColumnMap.length > 1); + int keyOutColumnNum; + int aggrOutColumnNum; + VectorOutputAggr outputAggr; + ColumnVector.Type inputColumnVectorType; + for (AggregationFunctionInfo aggrFuncInfo : aggrFuncInfos) { + + AggregationFunction aggregationFunction = aggrFuncInfo.getAggregationFunction(); + + // LOG.info("setupVectorOutput aggrFuncInfo " + aggrFuncInfo.toString()); + + aggrOutColumnNum = groupByKeyColumnMap.length + aggrFuncInfo.getFuncNum(); + + ColumnAggregationInfo columnAggrInfo = aggrFuncInfo.getColumnAggregationInfo(); + + outputAggr = null; + inputColumnVectorType = ColumnVector.Type.NONE; + if (columnAggrInfo == null) { + + // COUNT(*) + + outputAggr = new VectorOutputCountStarAggr(aggrOutColumnNum); + + } else if (aggregationFunction.getIsComplex()) { + + // UNDONE: e.g. AVG not handled yet. + throw new RuntimeException("Complex aggregation functions not supported yet"); + + } else { + + inputColumnVectorType = columnAggrInfo.getInputColumnVectorType(); + + if (columnAggrInfo.getIsKey()) { + + // Key aggregation is relatively trivial. + + keyOutColumnNum = columnAggrInfo.getKeyNum(); // Key number is the output key column num. + + if (aggregationFunction == AggregationFunction.COUNT_FUNC) { + + // Use COUNT(key) uses implicit COUNT(*) when key non-NULL. + if (isMultiKey) { + outputAggr = new VectorOutputCountKeyMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } else { + outputAggr = new VectorOutputCountKeySingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + + } else { + switch (inputColumnVectorType) { + case LONG: + switch (aggregationFunction) { + case MIN_FUNC: + case MAX_FUNC: + // Trivial copy key when key non-NULL. + if (isMultiKey) { + outputAggr = new VectorOutputLongKeyTrivialMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } else { + outputAggr = new VectorOutputLongKeyTrivialSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + break; + case SUM_FUNC: + // Use COUNT(*) multiplied by key value (when key non-NULL). + if (isMultiKey) { + outputAggr = new VectorOutputLongKeySumMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } else { + outputAggr = new VectorOutputLongKeySumSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + break; + default: + throw new RuntimeException("Aggregation function " + aggregationFunction.name() + + " not expected"); + } + break; + + case DOUBLE: + switch (aggregationFunction) { + case MIN_FUNC: + case MAX_FUNC: + // Trivial copy key when key non-NULL. + if (isMultiKey) { + outputAggr = new VectorOutputDoubleKeyTrivialMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } else { + outputAggr = new VectorOutputDoubleKeyTrivialSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + break; + case SUM_FUNC: + // Use COUNT(*) multiplied by key value (when key non-NULL). + if (isMultiKey) { + outputAggr = new VectorOutputDoubleKeySumMultiAggr(keyOutColumnNum, aggrOutColumnNum); + } else { + outputAggr = new VectorOutputDoubleKeySumSingleAggr(keyOutColumnNum, aggrOutColumnNum); + } + break; + default: + throw new RuntimeException("Aggregation function " + aggregationFunction.name() + + " not expected"); + } + break; + + default: + throw new RuntimeException("Unexpected column vector type " + inputColumnVectorType.name()); + } + } + } else { + + // Non-Key aggregation gets the aggregate by offset and copies it to the output. + // We must check if the aggregation entry has a value for the aggregation by checking + // the hasValues flags (bit number is the logicalNonKeyColumnNum). + + int logicalNonKeyColumnNum = columnAggrInfo.getLogicalNonKeyColumnNum(); + + NonKeyColumnAggregationInfo nonKeyColumnAggrInfo = + nonKeyColumnAggrInfoList.get(logicalNonKeyColumnNum); + + // The aggregation offsets for the basic aggregations on the column. + int[] basicAggregationOffsets = nonKeyColumnAggrInfo.getBasicAggregationOffsets(); + + BasicAggregation basicAggr; + int aggregateOffset; + if (aggregationFunction == AggregationFunction.COUNT_FUNC) { + + // COUNT(column) output column is Long. + basicAggr = longAggrFuncToBasicAggrMap.get(aggregationFunction); + + aggregateOffset = basicAggregationOffsets[basicAggr.value]; + + outputAggr = new VectorOutputLongNonKeyAggr(logicalNonKeyColumnNum, + aggregateOffset, hasValuesFlagsLongOffset, aggrOutColumnNum); + } else { + switch (inputColumnVectorType) { + case LONG: + basicAggr = longAggrFuncToBasicAggrMap.get(aggregationFunction); + + aggregateOffset = basicAggregationOffsets[basicAggr.value]; + + outputAggr = new VectorOutputLongNonKeyAggr(logicalNonKeyColumnNum, + aggregateOffset, hasValuesFlagsLongOffset, aggrOutColumnNum); + break; + + case DOUBLE: + basicAggr = doubleAggrFuncToBasicAggrMap.get(aggregationFunction); + + aggregateOffset = basicAggregationOffsets[basicAggr.value]; + + outputAggr = new VectorOutputDoubleNonKeyAggr(logicalNonKeyColumnNum, + aggregateOffset, hasValuesFlagsLongOffset, aggrOutColumnNum); + break; + + default: + throw new RuntimeException("Unexpected column vector type " + inputColumnVectorType.name()); + } + } + } + } + + vectorOutputAggrList.add(outputAggr); + } + + vectorOutputAggrs = vectorOutputAggrList.toArray(new VectorOutputAggr[0]); + + // LOG.info("setupVectorOutput countStartOffset " + countStarOffset); + // for (VectorOutputAggr longVectorOutput : vectorOutputAggrs) { + // LOG.info("setupVectorOutput longVectorOutput " + longVectorOutput.toString()); + // } + + } + + VectorOutputAggr[] vectorOutputAggrs; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByCommonOutput() { + super(); + } + + public VectorGroupByCommonOutput(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + List objectInspectors = new ArrayList(); + + List outputFieldNames = conf.getOutputColumnNames(); + + outputTypes = + new TypeInfo[groupByKeyTypeInfos.length + aggregationFunctionOutputTypeInfos.length]; + int outputTypesCount = 0; + + for(int i = 0; i < groupByKeyTypeInfos.length; ++i) { + outputTypes[outputTypesCount++] = groupByKeyTypeInfos[i]; + ObjectInspector objInsp = + TypeInfoUtils.getStandardWritableObjectInspectorFromTypeInfo( + groupByKeyTypeInfos[i]); + objectInspectors.add(objInsp); + } + + for (int i = 0; i < aggregationFunctionOutputTypeInfos.length; ++i) { + outputTypes[outputTypesCount++] = aggregationFunctionOutputTypeInfos[i]; + ObjectInspector objInsp = + TypeInfoUtils.getStandardWritableObjectInspectorFromTypeInfo( + aggregationFunctionOutputTypeInfos[i]); + objectInspectors.add(objInsp); + } + + standardOutputObjInspector = + ObjectInspectorFactory.getStandardStructObjectInspector(outputFieldNames, objectInspectors); + outputObjInspector = standardOutputObjInspector; + + /** + * Setup the output batch. + * + * NOTE: If we cannot do vectorized output, we still use the outputBatch while + * pulling information out of the hash table. In forwardOutputBatch, we extract rows + * and forward them one by one... + */ + vrbCtx = new VectorizedRowBatchCtx(); + vrbCtx.init(standardOutputObjInspector, vOutContext.getScratchColumnTypeNames()); + outputBatch = vrbCtx.createVectorizedRowBatch(); + if (!isVectorOutput) { + vectorExtractRowSameBatch = new VectorExtractRowSameBatch(); + vectorExtractRowSameBatch.init(outputTypes); + vectorExtractRowSameBatch.setOneBatch(outputBatch); + + singleRow = new Object[vectorExtractRowSameBatch.getCount()]; + } + + if (isOnlyCountStarLong) { + + // VectorGroupByLongCountStarHashTable optimizes by keeping COUNT(*) in hash table. + // The longs array is not used. + + + } else { + + // Setup our helper objects that copy aggregates in the storage arrays + // into the output batch. + + for (VectorOutputAggr vectorOutputAggr : vectorOutputAggrs) { + vectorOutputAggr.setBatch(outputBatch); + } + } + } + + protected void outputEntryAggregates(int entryBaseLongIndex, int startBatchIndex, + int count) { + + for (VectorOutputAggr longVectorOutput : vectorOutputAggrs) { + longVectorOutput.apply(aggrEntryAccess, entryBaseLongIndex, startBatchIndex, count); + } + } + + public void forwardOutputBatch(VectorizedRowBatch outputBatch) throws HiveException { + if (isVectorOutput) { + forward(outputBatch, null); + } else { + final int size = outputBatch.size; + List fields = standardOutputObjInspector.getAllStructFieldRefs(); + for (int batchIndex = 0; batchIndex < size; batchIndex++) { + vectorExtractRowSameBatch.extractRow(batchIndex, singleRow); + + // ReduceSinkOperator holds on to copies of objects, so we need to copy... + Object[] resultRow = new Object[singleRow.length]; + for (int i = 0; i < singleRow.length; i++) { + resultRow[i] = + ObjectInspectorUtils.copyToStandardObject( + singleRow[i], + fields.get(i).getFieldObjectInspector(), + ObjectInspectorCopyOption.WRITABLE); + } + forward(resultRow, outputObjInspector); + } + } + outputBatch.reset(); + } + + /** + * Copy all of the keys and aggregations to the output batch. + */ + protected abstract void outputGroupBy(boolean flushAllNullKey) throws HiveException; + + /** + * On close, make sure a partially filled overflow batch gets forwarded. + */ + @Override + public void closeOp(boolean aborted) throws HiveException { + super.closeOp(aborted); + if (!aborted) { + outputGroupBy(/* flushAllNullKey */ true); + if (outputBatch.size > 0) { + forwardOutputBatch(outputBatch); + } + } + if (isLogDebugEnabled) { + LOG.debug("VectorGroupByCommonOutputOperator closeOp " + batchCounter + " batches processed"); + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonStorage.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonStorage.java new file mode 100644 index 0000000..d7c10c7 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByCommonStorage.java @@ -0,0 +1,122 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.util.Arrays; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccessBaseImpl; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccessImpl; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc.HashTableKeyType; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * This class is common operator class for native vectorized group by for aggregation logic. + */ +public abstract class VectorGroupByCommonStorage extends VectorGroupByCommon { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonStorage.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + protected transient int nextLongIndex; + + protected transient int longArraySize; + private transient long[] longs; + + protected transient VectorGroupByAggrEntryAccess aggrEntryAccess; + + /* + * The longs index into the longs array. + */ + public final class LongsIndex { + public static final int bitLength = 32; + public static final long allBitsOn = (((long) 1) << bitLength) - 1; + public static final long bitMask = allBitsOn; + } + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByCommonStorage() { + super(); + } + + public VectorGroupByCommonStorage(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + // Unspecified parameters default to -1. + VectorGroupByAggrEntryAccessBaseImpl.Parameters parameters = + new VectorGroupByAggrEntryAccessBaseImpl.Parameters(); + parameters.entryLongLength = entryLongLength; + + if (isOnlyCountStarLong) { + + // VectorGroupByLongCountStarHashTable optimizes by keeping COUNT(*) in hash table. + // The longs array is not used. + aggrEntryAccess = + new VectorGroupByAggrEntryAccessImpl(parameters, null); + + } else { + + int logicalStorageSize = keyLimit; + + // The All NULLs entry at index 0 is automatically allocated. + nextLongIndex = entryLongLength; + + longArraySize = logicalStorageSize * entryLongLength; + longs = new long[longArraySize]; + + parameters.countStarLongOffset = countStarLongOffset; + parameters.hasValuesFlagsLongOffset = hasValuesFlagsLongOffset; + parameters.aggregationBaseLongOffset = aggregationBaseLongOffset; + parameters.keyBaseLongOffset = keyBaseLongOffset; + parameters.keysAreAllFixedLength = keysAreAllFixedLength; + parameters.entryLongLength = entryLongLength; + + aggrEntryAccess = + new VectorGroupByAggrEntryAccessImpl(parameters, longs); + + } + } + + protected void clearStorage(int startIndex) { + + Arrays.fill(longs, startIndex, longArraySize, 0); + + // Always start after the all NULLs entry. + nextLongIndex = startIndex; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalCountStarOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalCountStarOperator.java new file mode 100644 index 0000000..6b455e0 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalCountStarOperator.java @@ -0,0 +1,103 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized COUNT(*) only group by that is lookup on a single string + * using a specialized hash map. + */ +public class VectorGroupByGlobalCountStarOperator extends VectorGroupByCommonOutput { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByGlobalCountStarOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + private transient long countStarCount; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByGlobalCountStarOperator() { + super(); + } + + public VectorGroupByGlobalCountStarOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + countStarCount = 0; + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // COUNT(*) includes NULLs or just the batch size. + countStarCount += inputLogicalSize; + + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + ((LongColumnVector) outputBatch.cols[0]).vector[outputBatch.size++] = countStarCount; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalOperator.java new file mode 100644 index 0000000..ec75009 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByGlobalOperator.java @@ -0,0 +1,102 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized COUNT(*) only group by that is lookup on a single string + * using a specialized hash map. + */ +public class VectorGroupByGlobalOperator extends VectorGroupByCommonOutput { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByGlobalOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByGlobalOperator() { + super(); + } + + public VectorGroupByGlobalOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + chooseNonKeyBatchVariants(batch); + + // Aggregate into a single entry. + // + aggregate(/* startIndex */ 0, /* duplicateCount */ inputLogicalSize, /* entryBaseLongIndex */ 0); + + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + + // Output the single entry. + // + outputEntryAggregates(/* entryBaseLongIndex */ 0, /* startBatchIndex */ outputBatch.size++, /* count */ 1); + + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByKeyAsLongsHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByKeyAsLongsHashTable.java new file mode 100644 index 0000000..0902d14 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByKeyAsLongsHashTable.java @@ -0,0 +1,171 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * An bytes array key map optimized for vectorized only group by. + */ +public abstract class VectorGroupByKeyAsLongsHashTable extends VectorGroupByCommonHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonHashTable.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The find or create results for findOrCreateLongKey and findOrCreateNonLongKey. + + public transient boolean currentIsNewKey; + public transient int currentLongIndex; + public transient int currentDoublesIndex; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByKeyAsLongsHashTable() { + super(); + } + + public VectorGroupByKeyAsLongsHashTable(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + allocateBucketArray(BYTES_KEY_ENTRY_SIZE); + } + + //------------------------------------------------------------------------------------------------ + + private static int BYTES_KEY_ENTRY_SIZE = 2; + + public void findOrCreateKeyAsLongs(long[] keyAsLongs, int keyLongStart, int keyLongLength, + long hashCode, VectorGroupByAggrEntryAccess aggrEntryAccess) + throws HiveException, IOException { + + int intHashCode = (int) hashCode; + int slot = (intHashCode & logicalHashBucketMask); + long probeSlot = slot; + int i = 0; + boolean isNewKey; + int pairIndex = 0; + long entryBaseLongIndex = 0; + while (true) { + pairIndex = 2 * slot; + entryBaseLongIndex = slotMultiples[pairIndex]; + if (entryBaseLongIndex == 0) { + isNewKey = true; + break; + } + long tableHashCode = slotMultiples[pairIndex + 1]; + if (hashCode == tableHashCode) { + + // Since hash codes are not unique, we must compare bytes key to verify. + + if (aggrEntryAccess.keyAsLongsEquals( + (int) entryBaseLongIndex, keyAsLongs, keyLongStart, keyLongLength)) { + isNewKey = false; + break; + } + } + ++metricPutConflict; + // Some other key (collision) - keep probing. + probeSlot += (++i); + slot = (int)(probeSlot & logicalHashBucketMask); + } + + if (largestNumberOfSteps < i) { + if (isLogDebugEnabled) { + LOG.debug("Probed " + i + " slots (the longest so far) to find space"); + } + largestNumberOfSteps = i; + // debugDumpKeyProbe(keyOffset, keyLength, hashCode, slot); + } + + // LOG.debug("VectorGroupByCommonHashTableOperator findOrCreateNonLongKey slot " + slot + " hashCode " + Long.toHexString(hashCode)); + if (!isNewKey) { + + currentIsNewKey = false; + currentLongIndex = (int) entryBaseLongIndex; + + } else { + int newEntryLongLength; + if (keysAreAllFixedLength) { + newEntryLongLength = entryLongLength; + } else { + newEntryLongLength = aggrEntryAccess.varLenKeyEntryLongLength(keyLongLength); + } + boolean limitReached = + keyCount + 1 >= keyLimit || + !aggrEntryAccess.isRoomForNextEntry(nextLongIndex, newEntryLongLength); + + if (limitReached) { + + // We reached a limit. + + outputAggregationsAndClearHashTable(); + + if (nextLongIndex > longArraySize) { + throw new RuntimeException("Group by storage not cleared properly"); + } + + // Recalculate slot for hashCode since now the first slot must now be available. + slot = (intHashCode & logicalHashBucketMask); + pairIndex = 2 * slot; + if (slotMultiples[pairIndex] != 0) { + throw new RuntimeException("Group by hash table not cleared properly"); + } + } + slotMultiples[pairIndex] = nextLongIndex; + if (!keysAreAllFixedLength) { + aggrEntryAccess.setVarLenEntryLongLength(nextLongIndex, newEntryLongLength); + } + + currentLongIndex = nextLongIndex; + nextLongIndex += newEntryLongLength; + + aggrEntryAccess.copyKeyAsLongs(currentLongIndex, keyAsLongs, keyLongStart, + keyLongLength); + + currentIsNewKey = true; + + slotMultiples[pairIndex + 1] = hashCode; + + keyCount++; + } + } + + //------------------------------------------------------------------------------------------------ +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarHashTable.java new file mode 100644 index 0000000..17a691f --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarHashTable.java @@ -0,0 +1,162 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; +import java.util.Arrays; +import java.util.Collection; +import java.util.concurrent.Future; + +import org.apache.commons.logging.Log; +import org.apache.commons.logging.LogFactory; +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation.VectorGroupByAggrEntryAccess; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * An single long value map optimized for vector map join. + */ +public abstract class VectorGroupByLongCountStarHashTable extends VectorGroupByCommonHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByLongCountStarHashTable.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The find or create results for findOrCreateLongKey and findOrCreateNonLongKey. + + public transient boolean currentIsNewKey; + public transient int currentLongIndex; + public transient int currentDoublesIndex; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByLongCountStarHashTable() { + super(); + } + + public VectorGroupByLongCountStarHashTable(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + allocateBucketArray(LONG_KEY_COUNT_STAR_ENTRY_SIZE); + } + + //------------------------------------------------------------------------------------------------ + + private static int LONG_KEY_COUNT_STAR_ENTRY_SIZE = 2; + + public void findOrCreateLongKeyCountStar(long key, long hashCode, int count) + throws HiveException, IOException { + + int intHashCode = (int) hashCode; + int slot = (intHashCode & logicalHashBucketMask); + long probeSlot = slot; + int i = 0; + boolean isNewKey; + int pairIndex = 0; + while (true) { + pairIndex = 2 * slot; + if (slotMultiples[pairIndex + 1] == 0) { + isNewKey = true; + break; + } + if (key == slotMultiples[pairIndex]) { + isNewKey = false; + break; + } + ++metricPutConflict; + // Some other key (collision) - keep probing. + probeSlot += (++i); + slot = (int)(probeSlot & logicalHashBucketMask); + } + + if (largestNumberOfSteps < i) { + if (isLogDebugEnabled) { + LOG.debug("Probed " + i + " slots (the longest so far) to find space"); + } + largestNumberOfSteps = i; + // debugDumpKeyProbe(keyOffset, keyLength, hashCode, slot); + } + + if (isNewKey) { + boolean limitReached = (keyCount + 1 >= keyLimit); + if (limitReached) { + + // We reached a limit. + + outputAggregationsAndClearHashTable(); + + // Recalculate slot for hashCode since now the first slot must now be available. + slot = (intHashCode & logicalHashBucketMask); + pairIndex = 2 * slot; + if (slotMultiples[pairIndex] != 0) { + throw new RuntimeException("Group by hash table not cleared properly"); + } + } + slotMultiples[pairIndex] = key; + keyCount++; + } + slotMultiples[pairIndex + 1] += count; + } + + private int countStarPairIndex; + private long currentCountStartCount; + + protected int initLongCountStarIterator() { + countStarPairIndex = 0; + currentCountStartCount = 0; + return keyCount; + } + + // Find next key and return it. + protected long getLongCountStarKey() { + while (true) { + long count = slotMultiples[countStarPairIndex + 1]; + if (count > 0) { + currentCountStartCount = count; + long key = slotMultiples[countStarPairIndex]; + countStarPairIndex += 2; + return key; + } + countStarPairIndex += 2; + } + } + + public long getLongCountStarCount() { + return currentCountStartCount; + } + + //------------------------------------------------------------------------------------------------ + +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarOperator.java new file mode 100644 index 0000000..774c9ba --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongCountStarOperator.java @@ -0,0 +1,195 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized COUNT(*) only group by that is lookup on a single string + * using a specialized hash map. + */ +public class VectorGroupByLongCountStarOperator extends VectorGroupByLongCountStarHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByLongCountStarOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + private transient int singleKeyColumn; + private transient PrimitiveTypeInfo singleKeyColumnPrimitiveTypeInfo; + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + private transient long allNullCount; + + protected transient VectorKeySeriesLong longKeySeries; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByLongCountStarOperator() { + super(); + } + + public VectorGroupByLongCountStarOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + singleKeyColumn = groupByKeyColumnMap[0]; + singleKeyColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) groupByKeyTypeInfos[0]; + + allNullCount = 0; + + longKeySeries = new VectorKeySeriesLong(singleKeyColumn, singleKeyColumnPrimitiveTypeInfo); + } + + @Override + public void outputAggregationsAndClearHashTable() throws HiveException { + outputGroupBy(/* flushAllNullKey */ false); + if (outputBatch.size > 0) { + forwardOutputBatch(outputBatch); + } + clearHashTable(); + + // No storage to clear! + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // Perform any key expressions. Results will go into scratch columns. + if (groupByKeyExpressions != null) { + for (VectorExpression ve : groupByKeyExpressions) { + ve.evaluate(batch); + } + } + + longKeySeries.processBatch(batch); + + long key; + do { + if (longKeySeries.getCurrentIsAllNull()) { + + // Single null long key is not represented in the hash table. + + allNullCount += longKeySeries.getCurrentDuplicateCount(); + + atLeastAllNullKey = true; + + } else { + + key = longKeySeries.getCurrentKey(); + + // The count is stored in the hash table's slot array. + + findOrCreateLongKeyCountStar( + key, + longKeySeries.getCurrentHashCode(), + longKeySeries.getCurrentDuplicateCount()); + + } + + if (!longKeySeries.next()) { + break; + } + } while (true); + } catch (IOException e) { + throw new HiveException(e); + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + + // Keys come first in the output. + LongColumnVector longKeyColumnVector = (LongColumnVector) outputBatch.cols[0]; + + LongColumnVector countStarColumnVector = (LongColumnVector) outputBatch.cols[1]; + + // Handle NULL long key separately. + if (flushAllNullKey && atLeastAllNullKey) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + longKeyColumnVector.noNulls = false; + longKeyColumnVector.isNull[outputBatch.size] = true; + countStarColumnVector.vector[outputBatch.size++] = allNullCount; + } + + long[] keyVector = longKeyColumnVector.vector; + long[] countVector = countStarColumnVector.vector; + + int keyCount = initLongCountStarIterator(); + while (keyCount > 0) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + + int startBatchIndex = outputBatch.size; + int count = Math.min(keyCount, outputBatch.DEFAULT_SIZE - startBatchIndex); + + // Race down the slot table array and pull long key out of each entry and store into + // the output batch. + for (int i = startBatchIndex; i < startBatchIndex + count; i++) { + keyVector[i] = getLongCountStarKey(); + countVector[i] = getLongCountStarCount(); + } + outputBatch.size += count; + keyCount -= count; + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongHashTable.java new file mode 100644 index 0000000..480081e --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongHashTable.java @@ -0,0 +1,144 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * An single long key map optimized for vectorized only group by. + */ +public abstract class VectorGroupByLongHashTable extends VectorGroupByCommonHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByCommonHashTable.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The find or create results for findOrCreateLongKey and findOrCreateNonLongKey. + + public transient boolean currentIsNewKey; + public transient int currentLongIndex; + public transient int currentDoublesIndex; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByLongHashTable() { + super(); + } + + public VectorGroupByLongHashTable(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + allocateBucketArray(LONG_KEY_ENTRY_SIZE); + } + + private static int LONG_KEY_ENTRY_SIZE = 2; + + public void findOrCreateLongKey(long key, long hashCode) throws HiveException, IOException { + + int intHashCode = (int) hashCode; + int slot = (intHashCode & logicalHashBucketMask); + long probeSlot = slot; + int i = 0; + boolean isNewKey; + int pairIndex = 0; + long entryBaseLongIndex = 0; + while (true) { + pairIndex = 2 * slot; + entryBaseLongIndex = slotMultiples[pairIndex]; + if (entryBaseLongIndex == 0) { + isNewKey = true; + break; + } + long tableKey = slotMultiples[pairIndex + 1]; + if (key == tableKey) { + isNewKey = false; + break; + } + ++metricPutConflict; + // Some other key (collision) - keep probing. + probeSlot += (++i); + slot = (int)(probeSlot & logicalHashBucketMask); + } + + if (largestNumberOfSteps < i) { + if (isLogDebugEnabled) { + LOG.debug("Probed " + i + " slots (the longest so far) to find space"); + } + largestNumberOfSteps = i; + // debugDumpKeyProbe(keyOffset, keyLength, hashCode, slot); + } + + if (!isNewKey) { + + currentIsNewKey = false; + currentLongIndex = (int) entryBaseLongIndex; + + } else { + boolean limitReached = + keyCount + 1 >= keyLimit || + !aggrEntryAccess.isRoomForNextEntry(nextLongIndex, entryLongLength); + if (limitReached) { + + // We reached a limit. + + outputAggregationsAndClearHashTable(); + + if (nextLongIndex > longArraySize) { + throw new RuntimeException("Group by storage not cleared properly"); + } + + // Recalculate slot for hashCode since now the first slot must now be available. + slot = (intHashCode & logicalHashBucketMask); + pairIndex = 2 * slot; + if (slotMultiples[pairIndex] != 0) { + throw new RuntimeException("Group by hash table not cleared properly"); + } + } + slotMultiples[pairIndex] = currentLongIndex = nextLongIndex; + nextLongIndex += entryLongLength; + currentIsNewKey = true; + + slotMultiples[pairIndex + 1] = key; + + keyCount++; + } + } + + //------------------------------------------------------------------------------------------------ + +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongOperator.java new file mode 100644 index 0000000..6b6d6ab --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByLongOperator.java @@ -0,0 +1,208 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized group by that is lookup on a single long using a + * specialized hash map. + */ +public class VectorGroupByLongOperator extends VectorGroupByLongHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + private transient int singleKeyColumn; + private transient PrimitiveTypeInfo singleKeyColumnPrimitiveTypeInfo; + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + protected transient VectorKeySeriesLong longKeySeries; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByLongOperator() { + super(); + } + + public VectorGroupByLongOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + singleKeyColumn = groupByKeyColumnMap[0]; + singleKeyColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) groupByKeyTypeInfos[0]; + + longKeySeries = new VectorKeySeriesLong(singleKeyColumn, singleKeyColumnPrimitiveTypeInfo); + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // Perform any key expressions. Results will go into scratch columns. + if (groupByKeyExpressions != null) { + for (VectorExpression ve : groupByKeyExpressions) { + ve.evaluate(batch); + } + } + + chooseNonKeyBatchVariants(batch); + + longKeySeries.processBatch(batch); + + int entryBaseLongIndex; + long key; + do { + if (longKeySeries.getCurrentIsAllNull()) { + + // Single null long key is not represented in the hash table. + // We put its storage in entry 0. + + entryBaseLongIndex = 0; + + atLeastAllNullKey = true; + + } else { + + key = longKeySeries.getCurrentKey(); + + // The method findOrCreateLongKey sets its results in the member variables: + // currentIsNewKey, etc. + + findOrCreateLongKey(key, longKeySeries.getCurrentHashCode()); + + if (currentIsNewKey) { + + // We save the single long key in the entry as well as the slot table so when + // we are outputting we do not have to look at the slot table. We want to be able to + // just sequentially scan the storage entries in memory for good performance. + + aggrEntryAccess.putSingleLongKey(currentLongIndex, key); + } + + entryBaseLongIndex = currentLongIndex; + + } + + // The non-key batch variants array were set above to the current batch. + + aggregate( + longKeySeries.getCurrentLogical(), + longKeySeries.getCurrentDuplicateCount(), + entryBaseLongIndex); + + if (!longKeySeries.next()) { + break; + } + } while (true); + + forgetNonKeyBatchVariants(); + } catch (IOException e) { + throw new HiveException(e); + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + + // Keys come first in the output. + LongColumnVector longKeyColumnVector = (LongColumnVector) outputBatch.cols[0]; + + // Handle NULL long key separately. + if (flushAllNullKey && atLeastAllNullKey) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + longKeyColumnVector.noNulls = false; + longKeyColumnVector.isNull[outputBatch.size] = true; + outputEntryAggregates(/* longsIndex */ 0, outputBatch.size++, 1); + } + + // Loop over the arrays. + long[] vector = longKeyColumnVector.vector; + + // Determine the in-use count for current array (after NULL entry). + int currentCount = (nextLongIndex - entryLongLength) / entryLongLength; + + int longsIndex = entryLongLength; + + while (currentCount > 0) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + int startBatchIndex = outputBatch.size; + int count = Math.min(currentCount, outputBatch.DEFAULT_SIZE - startBatchIndex); + + // Race down the longs array and pull long key out of each entry and store into + // the output batch. Since this is a single key, we know there are no null keys. + int raceDownLongIndex = longsIndex; + for (int i = startBatchIndex; i < startBatchIndex + count; i++) { + vector[i] = aggrEntryAccess.getSingleLongKey(raceDownLongIndex); + raceDownLongIndex = aggrEntryAccess.nextEntryIndex(raceDownLongIndex); + } + + outputEntryAggregates(longsIndex, startBatchIndex, count); + longsIndex = raceDownLongIndex; + + outputBatch.size += count; + currentCount -= count; + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialCountStarOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialCountStarOperator.java new file mode 100644 index 0000000..83801f8 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialCountStarOperator.java @@ -0,0 +1,76 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a reduce-side merge-partial COUNT(*) only group by. + */ +public class VectorGroupByMergePartialCountStarOperator extends VectorGroupByMergePartialOperator { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByMergePartialOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + private transient long countStarCount; + private transient int countStarOutputColNum; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByMergePartialCountStarOperator() { + super(); + } + + public VectorGroupByMergePartialCountStarOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + countStarCount = 0; + countStarOutputColNum = groupByKeyColumnMap.length; + } + + protected void doMergePartialAggregations(VectorizedRowBatch batch) { + // COUNT(*) includes NULLs. + countStarCount += batch.size; + } + + @Override + protected void writeMergePartialAggregation() { + ((LongColumnVector) outputBatch.cols[countStarOutputColNum]).vector[outputBatch.size++] = countStarCount; + countStarCount = 0; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialOperator.java new file mode 100644 index 0000000..a2f7beb --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMergePartialOperator.java @@ -0,0 +1,176 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.VectorColumnOutputMapping; +import org.apache.hadoop.hive.ql.exec.vector.VectorCopyRow; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a reduce-side merge-partial group by. + */ +public class VectorGroupByMergePartialOperator extends VectorGroupByCommonOutput { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByMergePartialOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + private transient boolean inGroup; + private transient boolean first; + + private VectorCopyRow groupByKeyCopy; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByMergePartialOperator() { + super(); + } + + public VectorGroupByMergePartialOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + inGroup = false; + + groupByKeyCopy = new VectorCopyRow(); + VectorColumnOutputMapping groupByKeyMapping = new VectorColumnOutputMapping("Group By Key Mapping"); + for (int i = 0; i < groupByKeyColumnMap.length; i++) { + groupByKeyMapping.add(groupByKeyColumnMap[i], i, groupByKeyTypeInfos[i]); + } + groupByKeyMapping.finalize(); + + groupByKeyCopy.init(groupByKeyMapping); + + } + + @Override + public void startGroup() throws HiveException { + inGroup = true; + first = true; + } + + @Override + public void endGroup() throws HiveException { + if (inGroup && !first) { + writeMergePartialAggregation(); + } + inGroup = false; + } + + protected void doMergePartialAggregations(VectorizedRowBatch batch) { + + chooseNonKeyBatchVariants(batch); + + // Since there is one key per batch we aggregate the whole batch. + aggregate(/* startIndex */ 0, /* duplicateCount */ batch.size, /* entryBaseLongIndex */ 0); + } + + protected void writeMergePartialAggregation() { + outputEntryAggregates(/* entryBaseLongIndex */ 0, outputBatch.size++, 1); + clearStorage(/* startIndex */ 0); + } + + /** + * On close, make sure a partially filled overflow batch gets forwarded. + */ + @Override + public void closeOp(boolean aborted) throws HiveException { + super.closeOp(aborted); + if (!aborted) { + if (inGroup && !first) { + writeMergePartialAggregation(); + } + if (outputBatch.size > 0) { + forwardOutputBatch(outputBatch); + } + } + if (isLogDebugEnabled) { + LOG.debug("VectorGroupByMergePartialOperator closeOp " + batchCounter + " batches processed"); + } + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // Perform any key expressions. Results will go into scratch columns. + if (groupByKeyExpressions != null) { + for (VectorExpression ve : groupByKeyExpressions) { + ve.evaluate(batch); + } + } + + if (first) { + + // Copy the group key to output batch now. We'll copy in the aggregates at the + // end of the group. + first = false; + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + + // Batches from Reduce are repeating. + groupByKeyCopy.copyByValue(batch, 0, outputBatch, outputBatch.size); + } + + doMergePartialAggregations(batch); + + } catch (Exception e) { + throw new HiveException(e); + } + } + + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + // The closeOp method is overridden, so we don't use this one. + throw new RuntimeException("Not used"); + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMultiKeyFixedLenOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMultiKeyFixedLenOperator.java new file mode 100644 index 0000000..f6803e1 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByMultiKeyFixedLenOperator.java @@ -0,0 +1,284 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesCombo; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesDouble; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesSingle; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized group by that is lookup on multiple key columns (or a + * single non-long column) using a hash map. + */ +public class VectorGroupByMultiKeyFixedLenOperator extends VectorGroupByKeyAsLongsHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + // The object that determines equal key series. + private transient VectorKeySeriesCombo comboKeySeries; + + // UNDONE: For now, just make this a VectorKeySeriesLong instead of VectorKeySeries... + private transient VectorKeySeriesSingle[] keySeriesArray; + + private long[] keyAsLongs; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByMultiKeyFixedLenOperator() { + super(); + } + + public VectorGroupByMultiKeyFixedLenOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + comboKeySeries = new VectorKeySeriesCombo(); + keySeriesArray = new VectorKeySeriesSingle[groupByKeyColumnMap.length]; + for (int i = 0; i < groupByKeyColumnMap.length; i++) { + switch (groupByKeyColumnVectorTypes[i]) { + case LONG: + keySeriesArray[i] = + new VectorKeySeriesLong(groupByKeyColumnMap[i], (PrimitiveTypeInfo) groupByKeyTypeInfos[i]); + break; + case DOUBLE: + keySeriesArray[i] = + new VectorKeySeriesDouble(groupByKeyColumnMap[i], (PrimitiveTypeInfo) groupByKeyTypeInfos[i]); + break; + default: + throw new RuntimeException("Unexpected column vector type " + groupByKeyColumnVectorTypes[i].name()); + } + } + comboKeySeries.init(keySeriesArray); + + // NULL mask + N long keys. + keyAsLongs = new long[1 + groupByKeyColumnMap.length]; + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // Perform any key expressions. Results will go into scratch columns. + if (groupByKeyExpressions != null) { + for (VectorExpression ve : groupByKeyExpressions) { + ve.evaluate(batch); + } + } + + chooseNonKeyBatchVariants(batch); + + comboKeySeries.processBatch(batch); + + int keyAsLongsCount; + int hashCode; + int entryBaseLongIndex; + do { + if (comboKeySeries.getCurrentIsAllNull()) { + + // Single null long key is not represented in the hash table. + // We put its storage in entry 0. + + entryBaseLongIndex = 0; + + atLeastAllNullKey = true; + + } else { + + // Form fixed length long key array with NULL mask first. + keyAsLongs[0] = comboKeySeries.getCurrentNullMask(); + + keyAsLongsCount = 1; // Start after NULL key mask. + hashCode = 0; + for (int i = 0; i < groupByKeyColumnMap.length; i++) { + if (!keySeriesArray[i].getCurrentIsAllNull()) { + switch (groupByKeyColumnVectorTypes[i]) { + case LONG: + { + VectorKeySeriesLong longKeySeries = (VectorKeySeriesLong) keySeriesArray[i]; + keyAsLongs[keyAsLongsCount++] = longKeySeries.getCurrentKey(); + hashCode ^= longKeySeries.getCurrentHashCode(); + } + break; + case DOUBLE: + { + VectorKeySeriesDouble doubleKeySeries = (VectorKeySeriesDouble) keySeriesArray[i]; + keyAsLongs[keyAsLongsCount++] = doubleKeySeries.getCurrentDoubleAsLongKey(); + hashCode ^= doubleKeySeries.getCurrentHashCode(); + } + break; + default: + throw new RuntimeException("Unexpected column vector type " + groupByKeyColumnVectorTypes[i].name()); + } + } + } + + findOrCreateKeyAsLongs(keyAsLongs, 0, keyAsLongsCount, hashCode, aggrEntryAccess); + + entryBaseLongIndex = currentLongIndex; + + } + + // The non-key batch variants array were set above to the current batch. + + aggregate( + comboKeySeries.getCurrentLogical(), + comboKeySeries.getCurrentDuplicateCount(), + entryBaseLongIndex); + + if (!comboKeySeries.next()) { + break; + } + } while (true); + + forgetNonKeyBatchVariants(); + } catch (IOException e) { + throw new HiveException(e); + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + * @throws IOException + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + + try { + // Keys come first in the output. + + // Handle NULL long key separately. + if (flushAllNullKey && atLeastAllNullKey) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + for (int i = 0; i < groupByKeyColumnMap.length; i++) { + ColumnVector colVector = outputBatch.cols[i]; + colVector.noNulls = false; + colVector.isNull[outputBatch.size] = true; + } + outputEntryAggregates(/* longsIndex */ 0, outputBatch.size++, 1); + } + + // Loop over the arrays. + + // The hash table in-use count is key count (excluding the All NULL entry). + int currentCount = keyCount; + + // Start after All NULL entry. + int longsIndex = entryLongLength; + + long nullKeyMask; + int keyAsLongsCount; + ColumnVector colVector; + while (currentCount > 0) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + int startBatchIndex = outputBatch.size; + int count = Math.min(currentCount, outputBatch.DEFAULT_SIZE - startBatchIndex); + + // Race down the longs array and pull long key out of each entry and store into + // the output batch. Since this is a single key, we know there are no null keys. + int raceDownLongIndex = longsIndex; + for (int i = startBatchIndex; i < startBatchIndex + count; i++) { + + nullKeyMask = aggrEntryAccess.keyAsLongsGet(raceDownLongIndex, 0); + + keyAsLongsCount = 1; // Start after NULL key mask. + for (int k = 0; k < groupByKeyColumnMap.length; k++) { + + colVector = outputBatch.cols[k]; + if ((nullKeyMask & (1L << k)) == 0) { + + switch (groupByKeyColumnVectorTypes[k]) { + case LONG: + ((LongColumnVector) colVector).vector[k] = + aggrEntryAccess.keyAsLongsGet(raceDownLongIndex, keyAsLongsCount++); + break; + case DOUBLE: + ((DoubleColumnVector) colVector).vector[k] = + Double.longBitsToDouble( + aggrEntryAccess.keyAsLongsGet(raceDownLongIndex, keyAsLongsCount++)); + break; + default: + throw new RuntimeException("Unexpected column vector type " + groupByKeyColumnVectorTypes[k].name()); + } + } else { + colVector.noNulls = false; + colVector.isNull[i] = true; + } + } + + raceDownLongIndex = aggrEntryAccess.nextEntryIndex(raceDownLongIndex); + } + + outputEntryAggregates(longsIndex, startBatchIndex, count); + longsIndex = raceDownLongIndex; + + outputBatch.size += count; + currentCount -= count; + } + } catch (Exception e) { + throw new HiveException(e); + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByStringOperator.java new file mode 100644 index 0000000..14918d7 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/VectorGroupByStringOperator.java @@ -0,0 +1,199 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.io.IOException; + +import org.apache.hadoop.conf.Configuration; +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby.VectorKeySeriesBytesGroupBy; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * Specialized class for doing a vectorized group by that is lookup on a single string using a + * specialized hash map. + */ +public class VectorGroupByStringOperator extends VectorGroupByKeyAsLongsHashTable { + + private static final long serialVersionUID = 1L; + private static final String CLASS_NAME = VectorGroupByLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + private transient int singleKeyColumn; + private transient PrimitiveTypeInfo singleKeyColumnPrimitiveTypeInfo; + + // The above members are initialized by the constructor and must not be + // transient. + //--------------------------------------------------------------------------- + + protected transient VectorKeySeriesBytesGroupBy bytesKeySeries; + + //--------------------------------------------------------------------------- + // Pass-thru constructors. + // + + public VectorGroupByStringOperator() { + super(); + } + + public VectorGroupByStringOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + @Override + protected void initializeOp(Configuration hconf) throws HiveException { + super.initializeOp(hconf); + + singleKeyColumn = groupByKeyColumnMap[0]; + singleKeyColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) groupByKeyTypeInfos[0]; + + bytesKeySeries = new VectorKeySeriesBytesGroupBy(singleKeyColumn); + } + + @Override + public void process(Object row, int tag) throws HiveException { + + try { + VectorizedRowBatch batch = (VectorizedRowBatch) row; + + batchCounter++; + + final int inputLogicalSize = batch.size; + + if (inputLogicalSize == 0) { + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + } + return; + } + + // Perform any key expressions. Results will go into scratch columns. + if (groupByKeyExpressions != null) { + for (VectorExpression ve : groupByKeyExpressions) { + ve.evaluate(batch); + } + } + + chooseNonKeyBatchVariants(batch); + + bytesKeySeries.processBatch(batch); + + int entryBaseLongIndex; + do { + if (bytesKeySeries.getCurrentIsAllNull()) { + + // Single null long key is not represented in the hash table. + // We put its storage in entry 0. + + entryBaseLongIndex = 0; + + atLeastAllNullKey = true; + + } else { + + findOrCreateKeyAsLongs( + bytesKeySeries.getSerializedLongs(), + bytesKeySeries.getSerializedLongStart(), + bytesKeySeries.getSerializedLongLength(), + bytesKeySeries.getCurrentHashCode(), + aggrEntryAccess); + + entryBaseLongIndex = currentLongIndex; + + } + + // The non-key batch variants array were set above to the current batch. + + aggregate( + bytesKeySeries.getCurrentLogical(), + bytesKeySeries.getCurrentDuplicateCount(), + entryBaseLongIndex); + + if (!bytesKeySeries.next()) { + break; + } + } while (true); + + forgetNonKeyBatchVariants(); + } catch (IOException e) { + throw new HiveException(e); + } catch (Exception e) { + throw new HiveException(e); + } + } + + /** + * Flush all of the keys and aggregations to the output. + */ + @Override + protected void outputGroupBy(boolean flushAllNullKey) throws HiveException { + + // Keys come first in the output. + BytesColumnVector bytesKeyColumnVector = (BytesColumnVector) outputBatch.cols[0]; + + // Handle NULL long key separately. + if (flushAllNullKey && atLeastAllNullKey) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + bytesKeyColumnVector.noNulls = false; + bytesKeyColumnVector.isNull[outputBatch.size] = true; + outputEntryAggregates(/* longsIndex */ 0, outputBatch.size++, 1); + } + + // Loop over the arrays. + + // The in-use count is key count (excluding All NULL entry). + int currentCount = keyCount; + + // Start after All NULL entry. + int longsIndex = entryLongLength; + + while (currentCount > 0) { + if (outputBatch.size == outputBatch.DEFAULT_SIZE) { + forwardOutputBatch(outputBatch); + } + int startBatchIndex = outputBatch.size; + int count = Math.min(currentCount, outputBatch.DEFAULT_SIZE - startBatchIndex); + + // Race down the longs array and pull long key out of each entry and store into + // the output batch. Since this is a single key, we know there are no null keys. + int raceDownLongIndex = longsIndex; + for (int i = startBatchIndex; i < startBatchIndex + count; i++) { + aggrEntryAccess.copySingleLongsKeyToColumnVector( + raceDownLongIndex, bytesKeyColumnVector, i); + raceDownLongIndex = aggrEntryAccess.nextEntryIndex(raceDownLongIndex); + } + + outputEntryAggregates(longsIndex, startBatchIndex, count); + longsIndex = raceDownLongIndex; + + outputBatch.size += count; + currentCount -= count; + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccess.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccess.java new file mode 100644 index 0000000..c341e8a --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccess.java @@ -0,0 +1,71 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation; + +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; + +/** + * This interface is used by column aggregation to get/set keys, aggregates, etc. + */ +public interface VectorGroupByAggrEntryAccess { + + //------------------------------------------------------------------------------------------------ + + /** + * Add to the COUNT(*) counter. + * @param entryBaseLongIndex + * @param count + */ + void incrCountStar(int entryBaseLongIndex, int count); + + long getCountStar(int entryBaseLongIndex); + + long getHasValuesBitFlags(int entryBaseLongIndex); + void mergeHasValuesBitFlags(int entryBaseLongIndex, long newFlags); + + void putSingleLongKey(int entryBaseLongIndex, long key); + + long getSingleLongKey(int entryBaseLongIndex); + + long keyAsLongsGet(int entryBaseLongIndex, int offset); + + int varLenKeyEntryLongLength(int keyLongLength); + + boolean isRoomForNextEntry(int nextEntryBaseIndex, int nextEntryLongLength); + + void setVarLenEntryLongLength(int newEntryBaseLongIndex, int newEntryLongLength); + + void copyKeyAsLongs(int entryBaseLongIndex, long[] keyAsLongs, int keyLongStart, int keyLongLength); + + boolean keyAsLongsEquals(int entryBaseLongIndex, long[] keyAsLongs, int keyStart, int keyLength); + + void copySingleLongsKeyToColumnVector(int entryBaseLongIndex, + BytesColumnVector bytesColumnVector, int batchIndex); + + long getLongAggregationValue(int entryBaseLongIndex, int aggregationOffset); + + double getDoubleAggregationValue(int entryBaseLongIndex, int aggregationOffset); + + int nextEntryIndex(int entryBaseLongIndex); + + //------------------------------------------------------------------------------------------------ + + // Arrays memory representation + long[] getLongs(); +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessBaseImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessBaseImpl.java new file mode 100644 index 0000000..5c24efd --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessBaseImpl.java @@ -0,0 +1,60 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation; + +/** + * This class is base implementation for VectorGroupByAggrEntryAccess that is + * independent of the memory representation. + */ +public abstract class VectorGroupByAggrEntryAccessBaseImpl implements VectorGroupByAggrEntryAccess { + + public static class Parameters { + public int countStarLongOffset; + public int hasValuesFlagsLongOffset; + public int aggregationBaseLongOffset; + public int keyBaseLongOffset; + public boolean keysAreAllFixedLength; + public int entryLongLength; + + public Parameters() { + countStarLongOffset = -1; + hasValuesFlagsLongOffset = 1; + aggregationBaseLongOffset = -1; + keyBaseLongOffset = -1; + keysAreAllFixedLength = false; + entryLongLength = -1; + } + } + + protected final int countStarLongOffset; + protected final int hasValuesFlagsLongOffset; + protected final int aggregationBaseLongOffset; + protected final int keyBaseLongOffset; + protected final boolean keysAreAllFixedLength; + protected final int entryLongLength; + + VectorGroupByAggrEntryAccessBaseImpl(Parameters parameters) { + this.countStarLongOffset = parameters.countStarLongOffset; + this.hasValuesFlagsLongOffset = parameters.hasValuesFlagsLongOffset; + this.aggregationBaseLongOffset = parameters.aggregationBaseLongOffset; + this.keyBaseLongOffset = parameters.keyBaseLongOffset; + this.keysAreAllFixedLength = parameters.keysAreAllFixedLength; + this.entryLongLength = parameters.entryLongLength; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessImpl.java new file mode 100644 index 0000000..c17d5fc --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByAggrEntryAccessImpl.java @@ -0,0 +1,150 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation; + +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby.VectorGroupByLongsKey; + +/** + * This class is only memory representation implementation we have right now. + * + * It stores entries in two arrays: long and byte. + * + */ +public class VectorGroupByAggrEntryAccessImpl extends VectorGroupByAggrEntryAccessBaseImpl { + + private final long[] longs; + + public VectorGroupByAggrEntryAccessImpl(Parameters parameters, long[] longs) { + super(parameters); + this.longs = longs; + } + + @Override + public void incrCountStar(int entryBaseLongIndex, int duplicateCount) { + longs[entryBaseLongIndex + aggregationBaseLongOffset + countStarLongOffset] += duplicateCount; + } + + @Override + public long getCountStar(int entryBaseLongIndex) { + return longs[entryBaseLongIndex + aggregationBaseLongOffset + countStarLongOffset]; + } + + @Override + public long getHasValuesBitFlags(int entryBaseLongIndex) { + return longs[entryBaseLongIndex + hasValuesFlagsLongOffset]; + } + @Override + public void mergeHasValuesBitFlags(int entryBaseLongIndex, long newFlags) { + longs[entryBaseLongIndex + hasValuesFlagsLongOffset] |= newFlags; + } + + @Override + public void putSingleLongKey(int entryBaseLongIndex, long key) { + longs[entryBaseLongIndex + keyBaseLongOffset] = key; + } + @Override + public long getSingleLongKey(int entryBaseLongIndex) { + return longs[entryBaseLongIndex + keyBaseLongOffset]; + } + + @Override + public long keyAsLongsGet(int entryBaseLongIndex, int offset) { + return longs[entryBaseLongIndex + keyBaseLongOffset + offset]; + } + + @Override + public int varLenKeyEntryLongLength(int keyLongLength) { + return keyBaseLongOffset + keyLongLength; + } + + @Override + public boolean isRoomForNextEntry(int nextEntryBaseIndex, int nextEntryLongLength) { + return nextEntryBaseIndex + nextEntryLongLength < longs.length; + } + + @Override + public void setVarLenEntryLongLength(int newEntryBaseLongIndex, int newEntryLongLength) { + longs[newEntryBaseLongIndex] = newEntryLongLength; + } + + @Override + public void copyKeyAsLongs(int entryBaseLongIndex, long[] keyAsLongs, + int keyLongStart, int keyLongLength) { + System.arraycopy(keyAsLongs, keyLongStart, + longs, entryBaseLongIndex + keyBaseLongOffset, keyLongLength); + } + + @Override + public boolean keyAsLongsEquals(int entryBaseLongIndex, long[] keyAsLongs, + int keyLongStart, int keyLongLength) { + if (!keysAreAllFixedLength) { + int entryKeyLongLength = ((int) longs[entryBaseLongIndex]) - keyBaseLongOffset; + if (entryKeyLongLength != keyLongLength) { + return false; + } + } + + int entryKeyLongOffset = entryBaseLongIndex + keyBaseLongOffset; + for (int i = 0; i < keyLongLength; i++) { + if (longs[i + entryKeyLongOffset] != keyAsLongs[i + keyLongStart]) { + return false; + } + } + return true; + } + + @Override + public void copySingleLongsKeyToColumnVector(int entryBaseLongIndex, + BytesColumnVector bytesColumnVector, int batchIndex) { + int entryKeyLongOffset = entryBaseLongIndex + keyBaseLongOffset; + int keyByteLength = VectorGroupByLongsKey.byteLengthFromLongsKey(longs, entryKeyLongOffset); + bytesColumnVector.allocateVal(batchIndex, keyByteLength); + + // Copy bytes into allocated value within column vector. + VectorGroupByLongsKey.longsKeyToBytes(longs, entryKeyLongOffset, + bytesColumnVector.vector[batchIndex], bytesColumnVector.start[batchIndex]); + } + + @Override + public long getLongAggregationValue(int entryBaseLongIndex, int aggregationOffset) { + return longs[entryBaseLongIndex + aggregationBaseLongOffset + aggregationOffset]; + } + + @Override + public double getDoubleAggregationValue(int entryBaseLongIndex, int aggregationOffset) { + return Double.longBitsToDouble(longs[entryBaseLongIndex + aggregationBaseLongOffset + aggregationOffset]); + } + + @Override + public int nextEntryIndex(int entryBaseLongIndex) { + if (keysAreAllFixedLength) { + return entryBaseLongIndex + entryLongLength; + } else { + return entryBaseLongIndex + (int) longs[entryBaseLongIndex]; + } + } + + //------------------------------------------------------------------------------------------------ + + @Override + public long[] getLongs() { + return longs; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByColAggr.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByColAggr.java new file mode 100644 index 0000000..0aa151b --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/groupby/aggregation/VectorGroupByColAggr.java @@ -0,0 +1,123 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.aggregation; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; + +/** + * This abstract class is the base for all the variation of aggregatations in a column + * + */ +public abstract class VectorGroupByColAggr { + + // ORDER SENSITIVE --> Column aggregations does these basic aggregate operations in this exact + // order: + public enum BasicAggregation { + LONG_MIN ("Min", 0, 1 << 0, ColumnVector.Type.LONG), + DOUBLE_MIN ("DoubleMin", 1, 1 << 1, ColumnVector.Type.DOUBLE), + LONG_MAX ("Max", 2, 1 << 2, ColumnVector.Type.LONG), + DOUBLE_MAX ("DoubleMax", 3, 1 << 3, ColumnVector.Type.DOUBLE), + COUNT ("Count", 4, 1 << 4, ColumnVector.Type.LONG), + LONG_SUM ("Sum", 5, 1 << 5, ColumnVector.Type.LONG), + DOUBLE_SUM ("DoubleSum", 6, 1 << 6, ColumnVector.Type.DOUBLE); + + public final String string; + public final String variableName; + public final int value; + public final int mask; + public final ColumnVector.Type columnVectorType; + + public static final BasicAggregation[] basicAggregationValues = BasicAggregation.values(); + + BasicAggregation(String string, int value, int mask, ColumnVector.Type columnVectorType) { + this.string = string; + this.variableName = Character.toLowerCase(string.charAt(0)) + string.substring(1); + this.value = value; + this.mask = mask; + this.columnVectorType = columnVectorType; + } + } + + // Some batch attributes like repeating, selected, and nulls are for the whole input batch. + // We generate different aggregation classes for these variations, too. + // + public enum BatchVariation { + REPEATING_NONULLS (0, "RepeatingNoNulls"), + REPEATING_NULLS (1, "RepeatingNulls"), + SELECTED_NONULLS (2, "SelectedNoNulls"), + SELECTED_NULLS (3, "SelectedNulls"), + NONSELECTED_NONULLS (4, "NonSelectedNoNulls"), + NONSELECTED_NULLS (5, "NonSelectedNulls"); + + public final int index; + public final String string; + + public static final BatchVariation[] batchVariationValues = BatchVariation.values(); + + BatchVariation(int index, String string) { + this.index = index; + this.string = string; + } + } + + protected final int inputColumnNum; + + public VectorGroupByColAggr(int inputColumnNum) { + this.inputColumnNum = inputColumnNum; + } + + public int getInputColumnNum() { + return inputColumnNum; + } + + /** + * Used to setup any memory representation information needed. + * @param aggrEntryAccess + */ + public abstract void setMemoryRepresentation(VectorGroupByAggrEntryAccess aggrEntryAccess); + + /** + * Used to set the common column information used during to process a batch. + * @param batch + */ + public abstract void setColumnVectors(VectorizedRowBatch batch); + + /** + * Called after processing the batch to forget any common column information. + */ + public abstract void forgetColumnVectors(); + + /** + * Used by aggregation to know how many aggregates to skip after calling apply. + * + * @return the number of aggregates (min, max, count, sum, etc) for this variation. + */ + public abstract int getAggrCount(); + + /** + * @param startIndex Either the start batch index or start logical batch index. + * @param duplicateCount Number of duplicate keys. + * @param hasValues True if this aggregation entry already has values. + * @param colBaseAggrLongIndex The index into longs of the first aggregate of this column. + * @return New hasValues flag. + */ + public abstract boolean aggregateColumnAggregation(int startIndex, int duplicateCount, boolean hasValues, + int colBaseAggrLongIndex); +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytes.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytes.java new file mode 100644 index 0000000..5f1550e --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytes.java @@ -0,0 +1,95 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import org.apache.hive.common.util.HashCodeUtil; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of byte array keys. + * + */ +public class VectorKeySeriesBytes extends VectorKeySeriesBytesBase { + + private static final Logger LOG = LoggerFactory.getLogger(VectorKeySeriesBytes.class.getName()); + + private byte[][] keyBytesArrays; + private int[] keyStarts; + private int[] keyLengths; + + private byte[] currentBytes; + private int currentStart; + private int currentLength; + + public VectorKeySeriesBytes(int columnNum) { + super(columnNum); + keyBytesArrays = new byte[VectorizedRowBatch.DEFAULT_SIZE][]; + keyStarts = new int[VectorizedRowBatch.DEFAULT_SIZE]; + keyLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; + } + + public byte[] getCurrentBytes() { + return currentBytes; + } + + public int getCurrentStart() { + return currentStart; + } + + public int getCurrentLength() { + return currentLength; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + super.processBatch(batch); + + if (nonNullKeyCount > 0) { + HashCodeUtil.calculateBytesArrayHashCodes(keyBytesArrays, + keyStarts, keyLengths, hashCodes, nonNullKeyCount); + } + + // Do the position after we compute the checksums. + positionToFirst(); + } + + @Override + public void saveBytesKey(int nonNullKeyPosition, byte[] keyBytes, int keyByteStart, + int keyByteLength) { + keyBytesArrays[nonNullKeyPosition] = keyBytes; + keyStarts[nonNullKeyPosition] = keyByteStart; + keyLengths[nonNullKeyPosition] = keyByteLength; + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + currentBytes = keyBytesArrays[nonNullKeyPosition]; + currentStart = keyStarts[nonNullKeyPosition]; + currentLength = keyLengths[nonNullKeyPosition]; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesBase.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesBase.java new file mode 100644 index 0000000..ee5a8e2 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesBase.java @@ -0,0 +1,256 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of byte array keys where the keys get serialized using + * fast SerializeWrite style serialization. + */ +public abstract class VectorKeySeriesBytesBase extends VectorKeySeriesSingleImpl { + + private final int columnNum; + + public VectorKeySeriesBytesBase(int columnNum) { + super(); + this.columnNum = columnNum; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + + currentBatchSize = batch.size; + Preconditions.checkState(currentBatchSize > 0); + + BytesColumnVector bytesColVector = (BytesColumnVector) batch.cols[columnNum]; + + final byte[][] vector = bytesColVector.vector; + final int[] start = bytesColVector.start; + final int[] length = bytesColVector.length; + + if (bytesColVector.isRepeating){ + duplicateCounts[0] = currentBatchSize; + if (bytesColVector.noNulls || !bytesColVector.isNull[0]) { + seriesIsAllNull[0] = false; + saveBytesKey(0, vector[0], start[0], length[0]); + nonNullKeyCount = 1; + } else { + seriesIsAllNull[0] = true; + nonNullKeyCount = 0; + } + seriesCount = 1; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + seriesCount = 0; + nonNullKeyCount = 0; + if (batch.selectedInUse) { + int[] selected = batch.selected; + if (bytesColVector.noNulls) { + + duplicateCounts[0] = 1; + int index; + index = selected[0]; + byte[] prevKeyBytes = vector[index]; + int prevKeyStart = start[index]; + int prevKeyLength = length[index]; + saveBytesKey(0, prevKeyBytes, prevKeyStart, prevKeyLength); + + int currentKeyStart; + int currentKeyLength; + byte[] currentKeyBytes; + for (int logical = 1; logical < currentBatchSize; logical++) { + index = selected[logical]; + currentKeyBytes = vector[index]; + currentKeyStart = start[index]; + currentKeyLength = length[index]; + if (StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, + currentKeyBytes, currentKeyStart, currentKeyLength)) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveBytesKey(seriesCount, currentKeyBytes, currentKeyStart, currentKeyLength); + prevKeyBytes = currentKeyBytes; + prevKeyStart = currentKeyStart; + prevKeyLength = currentKeyLength; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = bytesColVector.isNull; + + boolean prevKeyIsNull; + byte[] prevKeyBytes = null; + int prevKeyStart = 0; + int prevKeyLength = 0; + duplicateCounts[0] = 1; + int index = selected[0]; + if (isNull[index]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKeyBytes = vector[index]; + prevKeyStart = start[index]; + prevKeyLength = length[index]; + saveBytesKey(0, prevKeyBytes, prevKeyStart, prevKeyLength); + nonNullKeyCount = 1; + } + + int currentKeyStart; + int currentKeyLength; + byte[] currentKeyBytes; + for (int logical = 1; logical < currentBatchSize; logical++) { + index = selected[logical]; + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + currentKeyBytes = vector[index]; + currentKeyStart = start[index]; + currentKeyLength = length[index]; + if (!prevKeyIsNull && + StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, + currentKeyBytes, currentKeyStart, currentKeyLength)) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveBytesKey(nonNullKeyCount++, currentKeyBytes, currentKeyStart, currentKeyLength); + prevKeyIsNull = false; + prevKeyBytes = currentKeyBytes; + prevKeyStart = currentKeyStart; + prevKeyLength = currentKeyLength; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } else { + + // NOT selectedInUse + + if (bytesColVector.noNulls) { + + duplicateCounts[0] = 1; + byte[] prevKeyBytes = vector[0]; + int prevKeyStart = start[0]; + int prevKeyLength = length[0]; + saveBytesKey(0, prevKeyBytes, prevKeyStart, prevKeyLength); + + int currentKeyStart; + int currentKeyLength; + byte[] currentKeyBytes; + for (int index = 1; index < currentBatchSize; index++) { + currentKeyBytes = vector[index]; + currentKeyStart = start[index]; + currentKeyLength = length[index]; + if (StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, + currentKeyBytes, currentKeyStart, currentKeyLength)) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveBytesKey(seriesCount, currentKeyBytes, currentKeyStart, currentKeyLength); + prevKeyBytes = currentKeyBytes; + prevKeyStart = currentKeyStart; + prevKeyLength = currentKeyLength; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = bytesColVector.isNull; + + boolean prevKeyIsNull; + byte[] prevKeyBytes = null; + int prevKeyStart = 0; + int prevKeyLength = 0; + duplicateCounts[0] = 1; + if (isNull[0]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKeyBytes = vector[0]; + prevKeyStart = start[0]; + prevKeyLength = length[0]; + saveBytesKey(0, prevKeyBytes, prevKeyStart, prevKeyLength); + nonNullKeyCount = 1; + } + + byte[] currentKeyBytes; + int currentKeyStart; + int currentKeyLength; + for (int index = 1; index < currentBatchSize; index++) { + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + currentKeyBytes = vector[index]; + currentKeyStart = start[index]; + currentKeyLength = length[index]; + if (!prevKeyIsNull && + StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, + currentKeyBytes, currentKeyStart, currentKeyLength)) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveBytesKey(nonNullKeyCount++, currentKeyBytes, currentKeyStart, currentKeyLength); + prevKeyIsNull = false; + prevKeyBytes = currentKeyBytes; + prevKeyStart = currentKeyStart; + prevKeyLength = currentKeyLength; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } + } + + Preconditions.checkState(validate()); + } + + protected abstract void saveBytesKey(int nonNullKeyPosition, byte[] keyBytes, int keyByteStart, + int keyByteLength) throws IOException; +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesSerialized.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesSerialized.java deleted file mode 100644 index 81a8455..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesBytesSerialized.java +++ /dev/null @@ -1,271 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.keyseries; - -import java.io.IOException; -import java.util.Arrays; - -import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; -import org.apache.hadoop.hive.serde2.fast.SerializeWrite; -import com.google.common.base.Preconditions; - -/** - * A key series of a single column of byte array keys where the keys get serialized. - */ -public class VectorKeySeriesBytesSerialized - extends VectorKeySeriesSerializedImpl implements VectorKeySeriesSerialized { - - private final int columnNum; - - private int outputStartPosition; - - public VectorKeySeriesBytesSerialized(int columnNum, T serializeWrite) { - super(serializeWrite); - this.columnNum = columnNum; - } - - @Override - public void processBatch(VectorizedRowBatch batch) throws IOException { - - currentBatchSize = batch.size; - Preconditions.checkState(currentBatchSize > 0); - - BytesColumnVector bytesColVector = (BytesColumnVector) batch.cols[columnNum]; - - byte[][] vectorBytesArrays = bytesColVector.vector; - int[] vectorStarts = bytesColVector.start; - int[] vectorLengths = bytesColVector.length; - - // The serialize routine uses this to build serializedKeyLengths. - outputStartPosition = 0; - output.reset(); - - if (bytesColVector.isRepeating){ - duplicateCounts[0] = currentBatchSize; - if (bytesColVector.noNulls || !bytesColVector.isNull[0]) { - seriesIsAllNull[0] = false; - serialize(0, vectorBytesArrays[0], vectorStarts[0], vectorLengths[0]); - nonNullKeyCount = 1; - } else { - seriesIsAllNull[0] = true; - nonNullKeyCount = 0; - } - seriesCount = 1; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - seriesCount = 0; - nonNullKeyCount = 0; - if (batch.selectedInUse) { - int[] selected = batch.selected; - if (bytesColVector.noNulls) { - - duplicateCounts[0] = 1; - int index; - index = selected[0]; - byte[] prevKeyBytes = vectorBytesArrays[index]; - int prevKeyStart = vectorStarts[index]; - int prevKeyLength = vectorLengths[index]; - serialize(0, prevKeyBytes, prevKeyStart, prevKeyLength); - - int currentKeyStart; - int currentKeyLength; - byte[] currentKeyBytes; - for (int logical = 1; logical < currentBatchSize; logical++) { - index = selected[logical]; - currentKeyBytes = vectorBytesArrays[index]; - currentKeyStart = vectorStarts[index]; - currentKeyLength = vectorLengths[index]; - if (StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, - currentKeyBytes, currentKeyStart, currentKeyLength)) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - serialize(seriesCount, currentKeyBytes, currentKeyStart, currentKeyLength); - prevKeyBytes = currentKeyBytes; - prevKeyStart = currentKeyStart; - prevKeyLength = currentKeyLength; - } - } - Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); - nonNullKeyCount = seriesCount; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - boolean[] isNull = bytesColVector.isNull; - - boolean prevKeyIsNull; - byte[] prevKeyBytes = null; - int prevKeyStart = 0; - int prevKeyLength = 0; - duplicateCounts[0] = 1; - int index = selected[0]; - if (isNull[index]) { - seriesIsAllNull[0] = true; - prevKeyIsNull = true; - } else { - seriesIsAllNull[0] = false; - prevKeyIsNull = false; - prevKeyBytes = vectorBytesArrays[index]; - prevKeyStart = vectorStarts[index]; - prevKeyLength = vectorLengths[index]; - serialize(0, prevKeyBytes, prevKeyStart, prevKeyLength); - nonNullKeyCount = 1; - } - - int currentKeyStart; - int currentKeyLength; - byte[] currentKeyBytes; - for (int logical = 1; logical < currentBatchSize; logical++) { - index = selected[logical]; - if (isNull[index]) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = true; - prevKeyIsNull = true; - } - } else { - currentKeyBytes = vectorBytesArrays[index]; - currentKeyStart = vectorStarts[index]; - currentKeyLength = vectorLengths[index]; - if (!prevKeyIsNull && - StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, - currentKeyBytes, currentKeyStart, currentKeyLength)) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = false; - serialize(nonNullKeyCount++, currentKeyBytes, currentKeyStart, currentKeyLength); - prevKeyIsNull = false; - prevKeyBytes = currentKeyBytes; - prevKeyStart = currentKeyStart; - prevKeyLength = currentKeyLength; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } - } else { - - // NOT selectedInUse - - if (bytesColVector.noNulls) { - - duplicateCounts[0] = 1; - byte[] prevKeyBytes = vectorBytesArrays[0]; - int prevKeyStart = vectorStarts[0]; - int prevKeyLength = vectorLengths[0]; - serialize(0, prevKeyBytes, prevKeyStart, prevKeyLength); - - int currentKeyStart; - int currentKeyLength; - byte[] currentKeyBytes; - for (int index = 1; index < currentBatchSize; index++) { - currentKeyBytes = vectorBytesArrays[index]; - currentKeyStart = vectorStarts[index]; - currentKeyLength = vectorLengths[index]; - if (StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, - currentKeyBytes, currentKeyStart, currentKeyLength)) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - serialize(seriesCount, currentKeyBytes, currentKeyStart, currentKeyLength); - prevKeyBytes = currentKeyBytes; - prevKeyStart = currentKeyStart; - prevKeyLength = currentKeyLength; - } - } - Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); - nonNullKeyCount = seriesCount; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - boolean[] isNull = bytesColVector.isNull; - - boolean prevKeyIsNull; - byte[] prevKeyBytes = null; - int prevKeyStart = 0; - int prevKeyLength = 0; - duplicateCounts[0] = 1; - if (isNull[0]) { - seriesIsAllNull[0] = true; - prevKeyIsNull = true; - } else { - seriesIsAllNull[0] = false; - prevKeyIsNull = false; - prevKeyBytes = vectorBytesArrays[0]; - prevKeyStart = vectorStarts[0]; - prevKeyLength = vectorLengths[0]; - serialize(0, prevKeyBytes, prevKeyStart, prevKeyLength); - nonNullKeyCount = 1; - } - - byte[] currentKeyBytes; - int currentKeyStart; - int currentKeyLength; - for (int index = 1; index < currentBatchSize; index++) { - if (isNull[index]) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = true; - prevKeyIsNull = true; - } - } else { - currentKeyBytes = vectorBytesArrays[index]; - currentKeyStart = vectorStarts[index]; - currentKeyLength = vectorLengths[index]; - if (!prevKeyIsNull && - StringExpr.equal(prevKeyBytes, prevKeyStart, prevKeyLength, - currentKeyBytes, currentKeyStart, currentKeyLength)) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = false; - serialize(nonNullKeyCount++, currentKeyBytes, currentKeyStart, currentKeyLength); - prevKeyIsNull = false; - prevKeyBytes = currentKeyBytes; - prevKeyStart = currentKeyStart; - prevKeyLength = currentKeyLength; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } - } - } - - // Finally. - computeSerializedHashCodes(); - positionToFirst(); - Preconditions.checkState(validate()); - } - - private void serialize(int pos, byte[] bytes, int start, int length) throws IOException { - serializeWrite.setAppend(output); - serializeWrite.writeString(bytes, start, length); - int outputNewPosition = output.getLength(); - serializedKeyLengths[pos] = outputNewPosition - outputStartPosition; - outputStartPosition = outputNewPosition; - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesCombo.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesCombo.java new file mode 100644 index 0000000..ab35e0f --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesCombo.java @@ -0,0 +1,138 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import com.google.common.base.Preconditions; + +/** + * A key series of a multiple columns of keys. + * (Or, it can be 1 column). + */ +public class VectorKeySeriesCombo extends VectorKeySeriesImpl implements VectorKeySeries { + + protected VectorKeySeriesSingle[] keySeriesArray; + + protected int currentBatchSize; + + private long allNullMask; + + protected long currentNullMask; + + public VectorKeySeriesCombo() { + super(); + } + + public void init(VectorKeySeriesSingle[] keySeriesArray) { + Preconditions.checkState(keySeriesArray.length > 0); + this.keySeriesArray = keySeriesArray; + allNullMask = (1L << keySeriesArray.length) - 1; + } + + @Override + public boolean getCurrentIsAllNull() { + return (currentNullMask == allNullMask); + } + + @Override + public boolean getCurrentHasAnyNulls() { + return (currentNullMask != 0); + } + + public long getCurrentNullMask() { + return currentNullMask; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + + currentBatchSize = batch.size; + Preconditions.checkState(currentBatchSize > 0); + + for (int i = 0; i < keySeriesArray.length; i ++) { + keySeriesArray[i].processBatch(batch); + } + + positionToFirst(); + } + + @Override + public void positionToFirst() { + currentLogical = 0; + + // Prime the pump with the first key. + keySeriesArray[0].positionToFirst(); + if (keySeriesArray[0].getCurrentIsAllNull()) { + currentNullMask = (1L << 0); + } else { + currentNullMask = 0; + } + currentDuplicateCount = keySeriesArray[0].getCurrentDuplicateCount(); + + // Determine the minimum series count. + for (int i = 1; i < keySeriesArray.length; i++) { + VectorKeySeriesSingle key = keySeriesArray[i]; + key.positionToFirst(); + if (key.getCurrentIsAllNull()) { + currentNullMask |= (1L << i); + } + currentDuplicateCount = Math.min(currentDuplicateCount, key.getCurrentDuplicateCount()); + } + Preconditions.checkState(currentDuplicateCount > 0); + Preconditions.checkState(currentDuplicateCount <= currentBatchSize); + } + + public boolean next() { + + currentLogical += currentDuplicateCount; + if (currentLogical >= currentBatchSize) { + return false; + } + + int prevDuplicateCount = currentDuplicateCount; + + // Advance past the series we just used. + keySeriesArray[0].advance(prevDuplicateCount); + + /* + * Calculate the next series. + */ + + // Prime the pump with the first key. + if (keySeriesArray[0].getCurrentIsAllNull()) { + currentNullMask = (1L << 0); + } else { + currentNullMask = 0; + } + currentDuplicateCount = keySeriesArray[0].getCurrentDuplicateCount(); + + // Determine the minimum series count. + for (int i = 1; i < keySeriesArray.length; i++) { + VectorKeySeriesSingle key = keySeriesArray[i]; + key.advance(prevDuplicateCount); + if (key.getCurrentIsAllNull()) { + currentNullMask |= (1L << i); + } + currentDuplicateCount = Math.min(currentDuplicateCount, key.getCurrentDuplicateCount()); + } + return true; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDouble.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDouble.java new file mode 100644 index 0000000..6ee241f --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDouble.java @@ -0,0 +1,75 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.apache.hive.common.util.HashCodeUtil; + +/** + * A key series of a single column of long keys. + */ +public class VectorKeySeriesDouble extends VectorKeySeriesDoubleBase { + + private final double[] doubleKeys; + private final long[] doubleAsLongKeys; + + private double currentDoubleKey; + private long currentDoubleAsLongKey; + + public VectorKeySeriesDouble(int columnNum, PrimitiveTypeInfo primitiveTypeInfo) { + super(columnNum, primitiveTypeInfo); + doubleKeys = new double[VectorizedRowBatch.DEFAULT_SIZE]; + doubleAsLongKeys = new long[VectorizedRowBatch.DEFAULT_SIZE]; + } + + public double getCurrentDoubleKey() { + return currentDoubleKey; + } + + public long getCurrentDoubleAsLongKey() { + return currentDoubleAsLongKey; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + super.processBatch(batch); + + if (nonNullKeyCount > 0) { + HashCodeUtil.calculateLongArrayHashCodes(doubleAsLongKeys, hashCodes, nonNullKeyCount); + } + + // Do the position after we compute the checksums. + positionToFirst(); + } + + @Override + protected void saveDoubleKey(int nonNullKeyPosition, double key) + throws IOException { + doubleKeys[nonNullKeyPosition] = key; + doubleAsLongKeys[nonNullKeyPosition] = Double.doubleToLongBits(key); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + currentDoubleKey = doubleKeys[nonNullKeyPosition]; + currentDoubleAsLongKey = doubleAsLongKeys[nonNullKeyPosition]; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDoubleBase.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDoubleBase.java new file mode 100644 index 0000000..fb573e9 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesDoubleBase.java @@ -0,0 +1,208 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.DoubleColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of long keys. + */ +public abstract class VectorKeySeriesDoubleBase extends VectorKeySeriesSingleImpl { + + protected final int columnNum; + protected final PrimitiveCategory primitiveCategory; + + public VectorKeySeriesDoubleBase(int columnNum, PrimitiveTypeInfo primitiveTypeInfo) { + super(); + this.columnNum = columnNum; + primitiveCategory = primitiveTypeInfo.getPrimitiveCategory(); + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + + currentBatchSize = batch.size; + Preconditions.checkState(currentBatchSize > 0); + + DoubleColumnVector doubleColVector = (DoubleColumnVector) batch.cols[columnNum]; + + double[] vector = doubleColVector.vector; + + if (doubleColVector.isRepeating){ + duplicateCounts[0] = currentBatchSize; + if (doubleColVector.noNulls || !doubleColVector.isNull[0]) { + seriesIsAllNull[0] = false; + saveDoubleKey(0, vector[0]); + nonNullKeyCount = 1; + } else { + seriesIsAllNull[0] = true; + nonNullKeyCount = 0; + } + seriesCount = 1; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + seriesCount = 0; + nonNullKeyCount = 0; + if (batch.selectedInUse) { + int[] selected = batch.selected; + if (doubleColVector.noNulls) { + + duplicateCounts[0] = 1; + double prevKey = vector[selected[0]]; + saveDoubleKey(0, prevKey); + + double currentKey; + for (int logical = 1; logical < currentBatchSize; logical++) { + currentKey = vector[selected[logical]]; + if (prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveDoubleKey(seriesCount, currentKey); + prevKey = currentKey; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = doubleColVector.isNull; + + boolean prevKeyIsNull; + double prevKey = 0; + duplicateCounts[0] = 1; + int index = selected[0]; + if (isNull[index]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKey = vector[index]; + saveDoubleKey(0, prevKey); + nonNullKeyCount = 1; + } + + double currentKey; + for (int logical = 1; logical < currentBatchSize; logical++) { + index = selected[logical]; + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + currentKey = vector[index]; + if (!prevKeyIsNull && prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveDoubleKey(nonNullKeyCount++, currentKey); + prevKeyIsNull = false; + prevKey = currentKey; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } else { + + // NOT selectedInUse + + if (doubleColVector.noNulls) { + + duplicateCounts[0] = 1; + double prevKey = vector[0]; + saveDoubleKey(0, prevKey); + double currentKey; + for (int index = 1; index < currentBatchSize; index++) { + currentKey = vector[index]; + if (prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveDoubleKey(seriesCount, currentKey); + prevKey = currentKey; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = doubleColVector.isNull; + + boolean prevKeyIsNull; + double prevKey = 0; + duplicateCounts[0] = 1; + if (isNull[0]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKey = vector[0]; + saveDoubleKey(nonNullKeyCount++, prevKey); + } + + for (int index = 1; index < currentBatchSize; index++) { + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + double currentKey = vector[index]; + if (!prevKeyIsNull && prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveDoubleKey(nonNullKeyCount++, currentKey); + prevKeyIsNull = false; + prevKey = currentKey; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } + } + + Preconditions.checkState(validate()); + } + + protected abstract void saveDoubleKey(int nonNullKeyPosition, double key) + throws IOException; +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLong.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLong.java new file mode 100644 index 0000000..c928201 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLong.java @@ -0,0 +1,66 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.apache.hive.common.util.HashCodeUtil; + +/** + * A key series of a single column of long keys. + */ +public class VectorKeySeriesLong extends VectorKeySeriesLongBase { + + private final long[] longKeys; + + private long currentKey; + + public VectorKeySeriesLong(int columnNum, PrimitiveTypeInfo primitiveTypeInfo) { + super(columnNum, primitiveTypeInfo); + longKeys = new long[VectorizedRowBatch.DEFAULT_SIZE]; + } + + public long getCurrentKey() { + return currentKey; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + super.processBatch(batch); + + if (nonNullKeyCount > 0) { + HashCodeUtil.calculateLongArrayHashCodes(longKeys, hashCodes, nonNullKeyCount); + } + + // Do the position after we compute the checksums. + positionToFirst(); + } + + @Override + protected void saveLongKey(int nonNullKeyPosition, long key) + throws IOException { + longKeys[nonNullKeyPosition] = key; + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + currentKey = longKeys[nonNullKeyPosition]; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongBase.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongBase.java new file mode 100644 index 0000000..01b8144 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongBase.java @@ -0,0 +1,208 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of long keys. + */ +public abstract class VectorKeySeriesLongBase extends VectorKeySeriesSingleImpl { + + protected final int columnNum; + protected final PrimitiveCategory primitiveCategory; + + public VectorKeySeriesLongBase(int columnNum, PrimitiveTypeInfo primitiveTypeInfo) { + super(); + this.columnNum = columnNum; + primitiveCategory = primitiveTypeInfo.getPrimitiveCategory(); + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + + currentBatchSize = batch.size; + Preconditions.checkState(currentBatchSize > 0); + + LongColumnVector longColVector = (LongColumnVector) batch.cols[columnNum]; + + long[] vector = longColVector.vector; + + if (longColVector.isRepeating){ + duplicateCounts[0] = currentBatchSize; + if (longColVector.noNulls || !longColVector.isNull[0]) { + seriesIsAllNull[0] = false; + saveLongKey(0, vector[0]); + nonNullKeyCount = 1; + } else { + seriesIsAllNull[0] = true; + nonNullKeyCount = 0; + } + seriesCount = 1; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + seriesCount = 0; + nonNullKeyCount = 0; + if (batch.selectedInUse) { + int[] selected = batch.selected; + if (longColVector.noNulls) { + + duplicateCounts[0] = 1; + long prevKey = vector[selected[0]]; + saveLongKey(0, prevKey); + + long currentKey; + for (int logical = 1; logical < currentBatchSize; logical++) { + currentKey = vector[selected[logical]]; + if (prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveLongKey(seriesCount, currentKey); + prevKey = currentKey; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = longColVector.isNull; + + boolean prevKeyIsNull; + long prevKey = 0; + duplicateCounts[0] = 1; + int index = selected[0]; + if (isNull[index]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKey = vector[index]; + saveLongKey(0, prevKey); + nonNullKeyCount = 1; + } + + long currentKey; + for (int logical = 1; logical < currentBatchSize; logical++) { + index = selected[logical]; + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + currentKey = vector[index]; + if (!prevKeyIsNull && prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveLongKey(nonNullKeyCount++, currentKey); + prevKeyIsNull = false; + prevKey = currentKey; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } else { + + // NOT selectedInUse + + if (longColVector.noNulls) { + + duplicateCounts[0] = 1; + long prevKey = vector[0]; + saveLongKey(0, prevKey); + long currentKey; + for (int index = 1; index < currentBatchSize; index++) { + currentKey = vector[index]; + if (prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + saveLongKey(seriesCount, currentKey); + prevKey = currentKey; + } + } + Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); + nonNullKeyCount = seriesCount; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + boolean[] isNull = longColVector.isNull; + + boolean prevKeyIsNull; + long prevKey = 0; + duplicateCounts[0] = 1; + if (isNull[0]) { + seriesIsAllNull[0] = true; + prevKeyIsNull = true; + } else { + seriesIsAllNull[0] = false; + prevKeyIsNull = false; + prevKey = vector[0]; + saveLongKey(nonNullKeyCount++, prevKey); + } + + for (int index = 1; index < currentBatchSize; index++) { + if (isNull[index]) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = true; + prevKeyIsNull = true; + } + } else { + long currentKey = vector[index]; + if (!prevKeyIsNull && prevKey == currentKey) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = false; + saveLongKey(nonNullKeyCount++, currentKey); + prevKeyIsNull = false; + prevKey = currentKey; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + } + } + + Preconditions.checkState(validate()); + } + + protected abstract void saveLongKey(int nonNullKeyPosition, long key) + throws IOException; +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongSerialized.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongSerialized.java deleted file mode 100644 index a0134fd..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesLongSerialized.java +++ /dev/null @@ -1,249 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.keyseries; - -import java.io.IOException; -import java.util.Arrays; - -import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.serde2.fast.SerializeWrite; -import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory; -import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; - -import com.google.common.base.Preconditions; - -/** - * A key series of a single column of long keys where the keys get serialized. - */ -public class VectorKeySeriesLongSerialized - extends VectorKeySeriesSerializedImpl implements VectorKeySeriesSerialized { - - private final int columnNum; - private PrimitiveCategory primitiveCategory; - - private int currentKeyStart; - - public VectorKeySeriesLongSerialized(int columnNum, PrimitiveTypeInfo primitiveTypeInfo, - T serializeWrite) { - super(serializeWrite); - this.columnNum = columnNum; - primitiveCategory = primitiveTypeInfo.getPrimitiveCategory(); - } - - @Override - public void processBatch(VectorizedRowBatch batch) throws IOException { - - currentBatchSize = batch.size; - Preconditions.checkState(currentBatchSize > 0); - - LongColumnVector longColVector = (LongColumnVector) batch.cols[columnNum]; - - long[] vector = longColVector.vector; - - // The serialize routine uses this to build serializedKeyLengths. - currentKeyStart = 0; - output.reset(); - - if (longColVector.isRepeating){ - duplicateCounts[0] = currentBatchSize; - if (longColVector.noNulls || !longColVector.isNull[0]) { - seriesIsAllNull[0] = false; - serialize(0, vector[0]); - nonNullKeyCount = 1; - } else { - seriesIsAllNull[0] = true; - nonNullKeyCount = 0; - } - seriesCount = 1; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - seriesCount = 0; - nonNullKeyCount = 0; - if (batch.selectedInUse) { - int[] selected = batch.selected; - if (longColVector.noNulls) { - - duplicateCounts[0] = 1; - long prevKey = vector[selected[0]]; - serialize(0, prevKey); - - long currentKey; - for (int logical = 1; logical < currentBatchSize; logical++) { - currentKey = vector[selected[logical]]; - if (prevKey == currentKey) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - serialize(seriesCount, currentKey); - prevKey = currentKey; - } - } - Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); - nonNullKeyCount = seriesCount; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - boolean[] isNull = longColVector.isNull; - - boolean prevKeyIsNull; - long prevKey = 0; - duplicateCounts[0] = 1; - int index = selected[0]; - if (isNull[index]) { - seriesIsAllNull[0] = true; - prevKeyIsNull = true; - nonNullKeyCount = 0; - } else { - seriesIsAllNull[0] = false; - prevKeyIsNull = false; - prevKey = vector[index]; - serialize(0, prevKey); - nonNullKeyCount = 1; - } - - long currentKey; - for (int logical = 1; logical < currentBatchSize; logical++) { - index = selected[logical]; - if (isNull[index]) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = true; - prevKeyIsNull = true; - } - } else { - currentKey = vector[index]; - if (!prevKeyIsNull && prevKey == currentKey) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = false; - serialize(nonNullKeyCount++, currentKey); - prevKeyIsNull = false; - prevKey = currentKey; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } - } else { - - // NOT selectedInUse - - if (longColVector.noNulls) { - - duplicateCounts[0] = 1; - long prevKey = vector[0]; - serialize(0, prevKey); - long currentKey; - for (int index = 1; index < currentBatchSize; index++) { - currentKey = vector[index]; - if (prevKey == currentKey) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - serialize(seriesCount, currentKey); - prevKey = currentKey; - } - } - Arrays.fill(seriesIsAllNull, 0, ++seriesCount, false); - nonNullKeyCount = seriesCount; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - boolean[] isNull = longColVector.isNull; - - boolean prevKeyIsNull; - long prevKey = 0; - duplicateCounts[0] = 1; - if (isNull[0]) { - seriesIsAllNull[0] = true; - prevKeyIsNull = true; - nonNullKeyCount = 0; - } else { - seriesIsAllNull[0] = false; - prevKeyIsNull = false; - prevKey = vector[0]; - serialize(0, prevKey); - nonNullKeyCount = 1; - } - - for (int index = 1; index < currentBatchSize; index++) { - if (isNull[index]) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = true; - prevKeyIsNull = true; - } - } else { - long currentKey = vector[index]; - if (!prevKeyIsNull && prevKey == currentKey) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = false; - serialize(nonNullKeyCount++, currentKey); - prevKeyIsNull = false; - prevKey = currentKey; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } - } - } - - // Finally. - computeSerializedHashCodes(); - positionToFirst(); - Preconditions.checkState(validate()); - } - - private void serialize(int pos, long value) throws IOException { - serializeWrite.setAppend(output); - - // UNDONE: Add support for DATE, TIMESTAMP, INTERVAL_YEAR_MONTH, INTERVAL_DAY_TIME... - switch (primitiveCategory) { - case BOOLEAN: - serializeWrite.writeBoolean(value != 0); - break; - case BYTE: - serializeWrite.writeByte((byte) value); - break; - case SHORT: - serializeWrite.writeShort((short) value); - break; - case INT: - serializeWrite.writeInt((int) value); - break; - case LONG: - serializeWrite.writeLong(value); - break; - default: - throw new RuntimeException("Unexpected primitive category " + primitiveCategory.name()); - } - int outputNewPosition = output.getLength(); - serializedKeyLengths[pos] = outputNewPosition - currentKeyStart; - currentKeyStart = outputNewPosition; - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiBase.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiBase.java new file mode 100644 index 0000000..69a839a --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiBase.java @@ -0,0 +1,152 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.serde2.fast.SerializeWrite; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/** + * A key series of a multiple columns of keys where the keys get serialized using + * fast SerializeWrite style serialization. (Or, it can be 1 column). + */ +public abstract class VectorKeySeriesMultiBase extends VectorKeySeriesSingleImpl { + + private static final Logger LOG = LoggerFactory.getLogger(VectorKeySeriesMultiBase.class.getName()); + + private boolean[] hasAnyNulls; + + public VectorKeySeriesMultiBase() { + super(); + hasAnyNulls = new boolean[VectorizedRowBatch.DEFAULT_SIZE]; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + + currentBatchSize = batch.size; + Preconditions.checkState(currentBatchSize > 0); + Preconditions.checkState(currentBatchSize <= VectorizedRowBatch.DEFAULT_SIZE); + + // LOG.info("VectorKeySeriesMultiBase processBatch size " + currentBatchSize + " numCols " + batch.numCols + " selectedInUse " + batch.selectedInUse); + + seriesCount = 0; + boolean prevKeyIsNull; + duplicateCounts[0] = 1; + if (batch.selectedInUse) { + int[] selected = batch.selected; + int index = selected[0]; + writeMultiKey(batch, index, 0); + if (isAllNulls()) { + seriesIsAllNull[0] = prevKeyIsNull = true; + nonNullKeyCount = 0; + } else { + seriesIsAllNull[0] = prevKeyIsNull = false; + hasAnyNulls[0] = hasAnyNulls(); + nonNullKeyCount = 1; + } + + for (int logical = 1; logical < currentBatchSize; logical++) { + index = selected[logical]; + writeMultiKey(batch, index, nonNullKeyCount); + if (isAllNulls()) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = prevKeyIsNull = true; + } + } else { + if (!prevKeyIsNull && equalsPrevKey(nonNullKeyCount)) { + forgetKey(); + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = prevKeyIsNull = false; + hasAnyNulls[nonNullKeyCount] = hasAnyNulls(); + nonNullKeyCount++; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } else { + writeMultiKey(batch, 0, 0); + if (isAllNulls()) { + seriesIsAllNull[0] = prevKeyIsNull = true; + nonNullKeyCount = 0; + } else { + seriesIsAllNull[0] = prevKeyIsNull = false; + hasAnyNulls[0] = hasAnyNulls(); + nonNullKeyCount = 1; + } + + for (int index = 1; index < currentBatchSize; index++) { + writeMultiKey(batch, index, nonNullKeyCount); + if (isAllNulls()) { + if (prevKeyIsNull) { + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = prevKeyIsNull = true; + } + } else { + if (!prevKeyIsNull && equalsPrevKey(nonNullKeyCount)) { + forgetKey(); + duplicateCounts[seriesCount]++; + } else { + duplicateCounts[++seriesCount] = 1; + seriesIsAllNull[seriesCount] = prevKeyIsNull = false; + hasAnyNulls[nonNullKeyCount] = hasAnyNulls(); + nonNullKeyCount++; + } + } + } + seriesCount++; + Preconditions.checkState(seriesCount <= currentBatchSize); + } + + Preconditions.checkState(validate()); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + currentHasAnyNulls = hasAnyNulls[nonNullKeyPosition]; + } + + protected abstract void writeMultiKey(VectorizedRowBatch batch, int index, int nonNullKeyCount) + throws IOException; + + protected abstract boolean isAllNulls(); + + protected abstract boolean hasAnyNulls(); + + protected abstract boolean equalsPrevKey(int nonNullKeyCount); + + protected abstract void forgetKey(); +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiSerialized.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiSerialized.java deleted file mode 100644 index 2cc3531..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesMultiSerialized.java +++ /dev/null @@ -1,187 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.keyseries; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; -import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.serde2.fast.SerializeWrite; -import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; - -import com.google.common.base.Preconditions; - -/** - * A key series of a multiple columns of keys where the keys get serialized. - * (Or, it can be 1 column). - */ -public class VectorKeySeriesMultiSerialized - extends VectorKeySeriesSerializedImpl implements VectorKeySeriesSerialized { - - private static final Logger LOG = LoggerFactory.getLogger( - VectorKeySeriesMultiSerialized.class.getName()); - - private VectorSerializeRow keySerializeRow; - - private boolean[] hasAnyNulls; - - public VectorKeySeriesMultiSerialized(T serializeWrite) { - super(serializeWrite); - } - - public void init(TypeInfo[] typeInfos, int[] columnNums) throws HiveException { - keySerializeRow = new VectorSerializeRow(serializeWrite); - keySerializeRow.init(typeInfos, columnNums); - hasAnyNulls = new boolean[VectorizedRowBatch.DEFAULT_SIZE]; - } - - @Override - public void processBatch(VectorizedRowBatch batch) throws IOException { - - currentBatchSize = batch.size; - Preconditions.checkState(currentBatchSize > 0); - - // LOG.info("VectorKeySeriesMultiSerialized processBatch size " + currentBatchSize + " numCols " + batch.numCols + " selectedInUse " + batch.selectedInUse); - - int prevKeyStart = 0; - int prevKeyLength; - int currentKeyStart = 0; - output.reset(); - - seriesCount = 0; - boolean prevKeyIsNull; - duplicateCounts[0] = 1; - if (batch.selectedInUse) { - int[] selected = batch.selected; - int index = selected[0]; - keySerializeRow.setOutputAppend(output); - keySerializeRow.serializeWrite(batch, index); - if (keySerializeRow.getIsAllNulls()) { - seriesIsAllNull[0] = prevKeyIsNull = true; - prevKeyLength = 0; - output.setWritePosition(0); - nonNullKeyCount = 0; - } else { - seriesIsAllNull[0] = prevKeyIsNull = false; - serializedKeyLengths[0] = currentKeyStart = prevKeyLength = output.getLength(); - hasAnyNulls[0] = keySerializeRow.getHasAnyNulls(); - nonNullKeyCount = 1; - } - - int keyLength; - for (int logical = 1; logical < currentBatchSize; logical++) { - index = selected[logical]; - keySerializeRow.setOutputAppend(output); - keySerializeRow.serializeWrite(batch, index); - if (keySerializeRow.getIsAllNulls()) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = prevKeyIsNull = true; - } - output.setWritePosition(currentKeyStart); - } else { - keyLength = output.getLength() - currentKeyStart; - if (!prevKeyIsNull && - StringExpr.equal( - output.getData(), prevKeyStart, prevKeyLength, - output.getData(), currentKeyStart, keyLength)) { - duplicateCounts[seriesCount]++; - output.setWritePosition(currentKeyStart); - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = prevKeyIsNull = false; - prevKeyStart = currentKeyStart; - serializedKeyLengths[nonNullKeyCount] = prevKeyLength = keyLength; - currentKeyStart += keyLength; - hasAnyNulls[nonNullKeyCount] = keySerializeRow.getHasAnyNulls(); - nonNullKeyCount++; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } else { - keySerializeRow.setOutputAppend(output); - keySerializeRow.serializeWrite(batch, 0); - if (keySerializeRow.getIsAllNulls()) { - seriesIsAllNull[0] = prevKeyIsNull = true; - prevKeyLength = 0; - output.setWritePosition(0); - nonNullKeyCount = 0; - } else { - seriesIsAllNull[0] = prevKeyIsNull = false; - serializedKeyLengths[0] = currentKeyStart = prevKeyLength = output.getLength(); - hasAnyNulls[0] = keySerializeRow.getHasAnyNulls(); - nonNullKeyCount = 1; - } - - int keyLength; - for (int index = 1; index < currentBatchSize; index++) { - keySerializeRow.setOutputAppend(output); - keySerializeRow.serializeWrite(batch, index); - if (keySerializeRow.getIsAllNulls()) { - if (prevKeyIsNull) { - duplicateCounts[seriesCount]++; - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = prevKeyIsNull = true; - } - output.setWritePosition(currentKeyStart); - } else { - keyLength = output.getLength() - currentKeyStart; - if (!prevKeyIsNull && - StringExpr.equal( - output.getData(), prevKeyStart, prevKeyLength, - output.getData(), currentKeyStart, keyLength)) { - duplicateCounts[seriesCount]++; - output.setWritePosition(currentKeyStart); - } else { - duplicateCounts[++seriesCount] = 1; - seriesIsAllNull[seriesCount] = prevKeyIsNull = false; - prevKeyStart = currentKeyStart; - serializedKeyLengths[nonNullKeyCount] = prevKeyLength = keyLength; - currentKeyStart += keyLength; - hasAnyNulls[nonNullKeyCount] = keySerializeRow.getHasAnyNulls(); - nonNullKeyCount++; - } - } - } - seriesCount++; - Preconditions.checkState(seriesCount <= currentBatchSize); - } - - // Finally. - computeSerializedHashCodes(); - positionToFirst(); - Preconditions.checkState(validate()); - } - - @Override - public void setNextNonNullKey(int nonNullKeyPosition) { - super.setNextNonNullKey(nonNullKeyPosition); - - currentHasAnyNulls = hasAnyNulls[nonNullKeyPosition]; - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerialized.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerialized.java deleted file mode 100644 index 1dfb3df..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerialized.java +++ /dev/null @@ -1,35 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.keyseries; - -/** - * An abstract adding serialization to key series. - * - * A key with no or some nulls has serialized bytes, offset, and length. - */ -public interface VectorKeySeriesSerialized extends VectorKeySeries { - - /** - * @return the serialized bytes (including optional key tag), start offset of the key in the - * bytes, and key byte length. - */ - byte[] getSerializedBytes(); - int getSerializedStart(); - int getSerializedLength(); -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerializedImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerializedImpl.java deleted file mode 100644 index 1fbafa7..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSerializedImpl.java +++ /dev/null @@ -1,130 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.keyseries; - -import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.serde2.ByteStream.Output; -import org.apache.hadoop.hive.serde2.fast.SerializeWrite; -import org.apache.hive.common.util.HashCodeUtil; - -import com.google.common.base.Preconditions; - -/** - * Implementation of base serialization interface. - * - */ -public abstract class VectorKeySeriesSerializedImpl - extends VectorKeySeriesSingleImpl implements VectorKeySeriesSerialized { - - protected T serializeWrite; - - protected int bufferOffset; - - // The serialized (non-NULL) series keys. These 3 members represent the value. - public int serializedStart; - public int serializedLength; - public byte[] serializedBytes; - - protected final Output output; - - protected final int[] serializedKeyLengths; - - public VectorKeySeriesSerializedImpl(T serializeWrite) { - super(); - this.serializeWrite = serializeWrite; - output = new Output(); - serializedKeyLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; - } - - public boolean validate() { - super.validate(); - - int nullCount = 0; - for (int i = 0; i < seriesCount; i++) { - if (seriesIsAllNull[i]) { - nullCount++; - } - } - Preconditions.checkState(nullCount + nonNullKeyCount == seriesCount); - - int lengthSum = 0; - int keyLength; - for (int i = 0; i < nonNullKeyCount; i++) { - keyLength = serializedKeyLengths[i]; - Preconditions.checkState(keyLength > 0); - lengthSum += keyLength; - Preconditions.checkState(lengthSum <= output.getLength()); - } - return true; - } - - @Override - public byte[] getSerializedBytes() { - return serializedBytes; - } - - @Override - public int getSerializedStart() { - return serializedStart; - } - - @Override - public int getSerializedLength() { - return serializedLength; - } - - /** - * Batch compute the hash codes for all the serialized keys. - * - * NOTE: MAJOR MAJOR ASSUMPTION: - * We assume that HashCodeUtil.murmurHash produces the same result - * as MurmurHash.hash with seed = 0 (the method used by ReduceSinkOperator for - * UNIFORM distribution). - */ - protected void computeSerializedHashCodes() { - int offset = 0; - int keyLength; - byte[] bytes = output.getData(); - for (int i = 0; i < nonNullKeyCount; i++) { - keyLength = serializedKeyLengths[i]; - hashCodes[i] = HashCodeUtil.murmurHash(bytes, offset, keyLength); - offset += keyLength; - } - } - - @Override - public void positionToFirst() { - - // Reset this before calling positionToFirst. - bufferOffset = 0; - - super.positionToFirst(); - - // This is constant for whole series. - serializedBytes = output.getData(); - } - - @Override - public void setNextNonNullKey(int nonNullKeyPosition) { - serializedStart = bufferOffset; - serializedLength = serializedKeyLengths[nonNullKeyPosition]; - Preconditions.checkState(serializedStart + serializedLength <= output.getData().length); - bufferOffset += serializedLength; - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingle.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingle.java new file mode 100644 index 0000000..f2017e8 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingle.java @@ -0,0 +1,33 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries; + +/** + * + */ +public interface VectorKeySeriesSingle extends VectorKeySeries { + + int getSeriesCount(); + + int getNonNullKeyCount(); + + void advance(int duplicateCount); + + void setNextNonNullKey(int nonNullKeyPosition); +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingleImpl.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingleImpl.java index bf0a25b..3a16a5e 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingleImpl.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/VectorKeySeriesSingleImpl.java @@ -28,7 +28,7 @@ * */ public abstract class VectorKeySeriesSingleImpl extends VectorKeySeriesImpl - implements VectorKeySeries { + implements VectorKeySeriesSingle { private static final Log LOG = LogFactory.getLog(VectorKeySeriesSingleImpl.class.getName()); @@ -55,7 +55,7 @@ // The hash code for each non-NULL key. protected final int[] hashCodes; - VectorKeySeriesSingleImpl() { + protected VectorKeySeriesSingleImpl() { super(); seriesCount = 0; @@ -70,6 +70,16 @@ hashCodes = new int[VectorizedRowBatch.DEFAULT_SIZE]; } + @Override + public int getSeriesCount() { + return seriesCount; + } + + @Override + public int getNonNullKeyCount() { + return nonNullKeyCount; + } + public boolean validate() { Preconditions.checkState(seriesCount > 0); Preconditions.checkState(seriesCount <= currentBatchSize); @@ -136,6 +146,7 @@ public boolean next() { } // For use by VectorKeySeriesMulti so that the minimum equal key can be advanced. + @Override public void advance(int duplicateCount) { currentLogical += currentDuplicateCount; @@ -154,5 +165,5 @@ public void advance(int duplicateCount) { } } - protected abstract void setNextNonNullKey(int nonNullKeyPosition); + public abstract void setNextNonNullKey(int nonNullKeyPosition); } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesBytesFast.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesBytesFast.java new file mode 100644 index 0000000..03a604f --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesBytesFast.java @@ -0,0 +1,121 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.fast; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytesBase; +import org.apache.hadoop.hive.serde2.ByteStream.Output; +import org.apache.hadoop.hive.serde2.fast.SerializeWrite; +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of byte array keys where the keys get serialized using + * fast SerializeWrite style serialization. + */ +public class VectorKeySeriesBytesFast + extends VectorKeySeriesBytesBase implements VectorKeySeriesFast { + + private T serializeWrite; + + // The serialized (non-NULL) series keys. These 3 members represent the value. + private byte[] serializedBytes; + private int serializedStart; + private int serializedLength; + + private final Output output; + + private int outputOffset; + + private final int[] serializedKeyLengths; + + public VectorKeySeriesBytesFast(int columnNum, T serializeWrite) { + super(columnNum); + this.serializeWrite = serializeWrite; + output = new Output(); + serializedKeyLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; + } + + @Override + public byte[] getSerializedBytes() { + return serializedBytes; + } + + @Override + public int getSerializedStart() { + return serializedStart; + } + + @Override + public int getSerializedLength() { + return serializedLength; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + outputOffset = 0; + output.reset(); + + super.processBatch(batch); + + Preconditions.checkState( + VectorKeySeriesFastUtil.validate(seriesCount, nonNullKeyCount, seriesIsAllNull, + serializedKeyLengths, output.getLength())); + + if (nonNullKeyCount > 0) { + // Compute hash codes of fast serialized keys. + VectorKeySeriesFastUtil.computeSerializedHashCodes(output, serializedKeyLengths, + nonNullKeyCount, hashCodes); + } + + // Do the posiiton after we compute the checksums. + positionToFirst(); + } + + @Override + public void saveBytesKey(int nonNullKeyPosition, byte[] keyBytes, int keyByteStart, + int keyByteLength) throws IOException { + serializeWrite.setAppend(output); + serializeWrite.writeString(keyBytes, keyByteStart, keyByteLength); + int outputNewOffset = output.getLength(); + serializedKeyLengths[nonNullKeyPosition] = outputNewOffset - outputOffset; + outputOffset = outputNewOffset; + } + + @Override + public void positionToFirst() { + + // Reset this before calling positionToFirst. + outputOffset = 0; + + super.positionToFirst(); + + // This is constant for whole series. + serializedBytes = output.getData(); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + serializedStart = outputOffset; + serializedLength = serializedKeyLengths[nonNullKeyPosition]; + Preconditions.checkState(serializedStart + serializedLength <= output.getData().length); + outputOffset += serializedLength; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFast.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFast.java new file mode 100644 index 0000000..53cbfff --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFast.java @@ -0,0 +1,37 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.fast; + +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeries; + +/** + * An abstract adding to key series the fast SerializeWrite style serialization. + * + * A key with no or some nulls has serialized bytes, offset, and length. + */ +public interface VectorKeySeriesFast extends VectorKeySeries { + + /** + * @return the serialized bytes, start offset of the key in the + * bytes, and key byte length. + */ + byte[] getSerializedBytes(); + int getSerializedStart(); + int getSerializedLength(); +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFastUtil.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFastUtil.java new file mode 100644 index 0000000..58ba674 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesFastUtil.java @@ -0,0 +1,87 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.fast; + +import java.util.Arrays; + +import org.apache.hadoop.hive.serde2.ByteStream.Output; +import org.apache.hive.common.util.HashCodeUtil; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/** + * Implementation of base fast SerializeWrite style serialization interface. + * + */ +public class VectorKeySeriesFastUtil { + + private static final String CLASS_NAME = VectorKeySeriesFastUtil.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + public static boolean validate(int seriesCount, int nonNullKeyCount, boolean[] seriesIsAllNull, + int[] serializedKeyLengths, int outputUsedLength) { + + int nullCount = 0; + for (int i = 0; i < seriesCount; i++) { + if (seriesIsAllNull[i]) { + nullCount++; + } + } + Preconditions.checkState(nullCount + nonNullKeyCount == seriesCount); + + Preconditions.checkState(validateKeyLengthSum(nonNullKeyCount, serializedKeyLengths, outputUsedLength)); + + return true; + } + + public static boolean validateKeyLengthSum(int nonNullKeyCount, + int[] serializedKeyLengths, int outputUsedLength) { + int lengthSum = 0; + int keyLength; + for (int i = 0; i < nonNullKeyCount; i++) { + keyLength = serializedKeyLengths[i]; + Preconditions.checkState(keyLength > 0); + lengthSum += keyLength; + Preconditions.checkState(lengthSum <= outputUsedLength); + } + return true; + } + + /** + * Batch compute the hash codes for all the serialized keys. + * + * NOTE: MAJOR MAJOR ASSUMPTION: + * We assume that HashCodeUtil.murmurHash produces the same result + * as MurmurHash.hash with seed = 0 (the method used by ReduceSinkOperator for + * UNIFORM distribution). + */ + protected static void computeSerializedHashCodes(Output output, int[] serializedKeyLengths, + int nonNullKeyCount, int[] hashCodes) { + int offset = 0; + int keyLength; + byte[] bytes = output.getData(); + for (int i = 0; i < nonNullKeyCount; i++) { + keyLength = serializedKeyLengths[i]; + hashCodes[i] = HashCodeUtil.murmurHash(bytes, offset, keyLength); + offset += keyLength; + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesLongFast.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesLongFast.java new file mode 100644 index 0000000..2e47148 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesLongFast.java @@ -0,0 +1,147 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.fast; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytesBase; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLongBase; +import org.apache.hadoop.hive.serde2.ByteStream.Output; +import org.apache.hadoop.hive.serde2.fast.SerializeWrite; +import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; + +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of long keys where the keys get serialized using + * fast SerializeWrite style serialization + */ +public class VectorKeySeriesLongFast + extends VectorKeySeriesLongBase implements VectorKeySeriesFast { + + private T serializeWrite; + + // The serialized (non-NULL) series keys. These 3 members represent the value. + private byte[] serializedBytes; + private int serializedStart; + private int serializedLength; + + private final Output output; + + private int outputOffset; + + private final int[] serializedKeyLengths; + + public VectorKeySeriesLongFast(int columnNum, PrimitiveTypeInfo primitiveTypeInfo, + T serializeWrite) { + super(columnNum, primitiveTypeInfo); + this.serializeWrite = serializeWrite; + output = new Output(); + serializedKeyLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; + } + + + @Override + public byte[] getSerializedBytes() { + return serializedBytes; + } + + @Override + public int getSerializedStart() { + return serializedStart; + } + + @Override + public int getSerializedLength() { + return serializedLength; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + outputOffset = 0; + output.reset(); + + super.processBatch(batch); + + Preconditions.checkState( + VectorKeySeriesFastUtil.validate(seriesCount, nonNullKeyCount, seriesIsAllNull, + serializedKeyLengths, output.getLength())); + + if (nonNullKeyCount > 0) { + // Compute hash codes of fast serialized keys. + VectorKeySeriesFastUtil.computeSerializedHashCodes(output, serializedKeyLengths, + nonNullKeyCount, hashCodes); + } + + // Do the posiiton after we compute the checksums. + positionToFirst(); + } + + protected void saveLongKey(int nonNullKeyPosition, long key) throws IOException { + serializeWrite.setAppend(output); + + // UNDONE: Add support for DATE, TIMESTAMP, INTERVAL_YEAR_MONTH, INTERVAL_DAY_TIME... + switch (primitiveCategory) { + case BOOLEAN: + serializeWrite.writeBoolean(key != 0); + break; + case BYTE: + serializeWrite.writeByte((byte) key); + break; + case SHORT: + serializeWrite.writeShort((short) key); + break; + case INT: + serializeWrite.writeInt((int) key); + break; + case LONG: + serializeWrite.writeLong(key); + break; + default: + throw new RuntimeException("Unexpected primitive category " + primitiveCategory.name()); + } + int outputNewPosition = output.getLength(); + serializedKeyLengths[nonNullKeyPosition] = outputNewPosition - outputOffset; + outputOffset = outputNewPosition; + } + + @Override + public void positionToFirst() { + + // Reset this before calling positionToFirst. + outputOffset = 0; + + super.positionToFirst(); + + // This is constant for whole series. + serializedBytes = output.getData(); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + serializedStart = outputOffset; + serializedLength = serializedKeyLengths[nonNullKeyPosition]; + Preconditions.checkState(serializedStart + serializedLength <= output.getData().length); + outputOffset += serializedLength; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesMultiFast.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesMultiFast.java new file mode 100644 index 0000000..3888d60 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/fast/VectorKeySeriesMultiFast.java @@ -0,0 +1,171 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.fast; + +import java.io.IOException; +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesMultiBase; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.serde2.ByteStream.Output; +import org.apache.hadoop.hive.serde2.fast.SerializeWrite; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import com.google.common.base.Preconditions; + +/** + * A key series of a multiple columns of keys where the keys get serialized using + * fast SerializeWrite style serialization. (Or, it can be 1 column). + */ +public class VectorKeySeriesMultiFast + extends VectorKeySeriesMultiBase implements VectorKeySeriesFast { + + private static final String CLASS_NAME = VectorKeySeriesMultiFast.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + private final T serializeWrite; + + private VectorSerializeRow keySerializeRow; + + // The serialized (non-NULL) series keys. These 3 members represent the value. + private int serializedStart; + private int serializedLength; + private byte[] serializedBytes; + + private final Output output; + + private int currentKeyOffset; + + private final int[] serializedKeyLengths; + + public VectorKeySeriesMultiFast(T serializeWrite) { + this.serializeWrite = serializeWrite; + output = new Output(); + serializedKeyLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; + } + + public void init(TypeInfo[] typeInfos, int[] columnNums) throws HiveException { + keySerializeRow = new VectorSerializeRow(serializeWrite); + keySerializeRow.init(typeInfos, columnNums); + } + + @Override + public byte[] getSerializedBytes() { + return serializedBytes; + } + + @Override + public int getSerializedStart() { + return serializedStart; + } + + @Override + public int getSerializedLength() { + return serializedLength; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + currentKeyOffset = 0; + output.reset(); + + super.processBatch(batch); + + Preconditions.checkState( + VectorKeySeriesFastUtil.validate(seriesCount, nonNullKeyCount, seriesIsAllNull, + serializedKeyLengths, output.getLength())); + + if (nonNullKeyCount > 0) { + // Compute hash codes of fast serialized keys. + VectorKeySeriesFastUtil.computeSerializedHashCodes(output, serializedKeyLengths, + nonNullKeyCount, hashCodes); + } + + // Do the position after we compute the checksums. + positionToFirst(); + } + + @Override + protected void writeMultiKey(VectorizedRowBatch batch, int index, int nonNullKeyCount) + throws IOException { + currentKeyOffset = output.getLength(); + keySerializeRow.setOutputAppend(output); + keySerializeRow.serializeWrite(batch, index); + if (keySerializeRow.getIsAllNulls()) { + output.setWritePosition(currentKeyOffset); + return; + } + + serializedKeyLengths[nonNullKeyCount] = output.getLength() - currentKeyOffset; + // Preconditions.checkState( + // VectorKeySeriesFastUtil.validateKeyLengthSum(nonNullKeyCount + 1, serializedKeyLengths, + // output.getLength())); + } + + @Override + protected boolean isAllNulls() { + return keySerializeRow.getIsAllNulls(); + } + + @Override + protected boolean hasAnyNulls() { + return keySerializeRow.getHasAnyNulls(); + } + + @Override + protected boolean equalsPrevKey(int nonNullKeyCount) { + int prevKeyLength = serializedKeyLengths[nonNullKeyCount - 1]; + int keyLength = serializedKeyLengths[nonNullKeyCount]; + byte[] bytes = output.getData(); + boolean result = + StringExpr.equal(bytes, currentKeyOffset - prevKeyLength, prevKeyLength, + bytes, currentKeyOffset, keyLength); + return result; + } + + @Override + protected void forgetKey() { + output.setWritePosition(currentKeyOffset); + } + + @Override + public void positionToFirst() { + + // Reset this before calling positionToFirst. + currentKeyOffset = 0; + + super.positionToFirst(); + + // This is constant for whole series. + serializedBytes = output.getData(); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + serializedStart = currentKeyOffset; + serializedLength = serializedKeyLengths[nonNullKeyPosition]; + Preconditions.checkState(serializedStart + serializedLength <= output.getData().length); + currentKeyOffset += serializedLength; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorGroupByLongsKey.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorGroupByLongsKey.java new file mode 100644 index 0000000..123bb29 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorGroupByLongsKey.java @@ -0,0 +1,206 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/* + * + */ +public final class VectorGroupByLongsKey { + + private static final String CLASS_NAME = VectorGroupByLongsKey.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + public static int LONG_BYTE_SIZE = Long.SIZE / Byte.SIZE; + + public static int longsKeyLengthFromByteLength(int byteLength) { + // Currently, just use the a whole long for byte length. + return 1 + (byteLength + LONG_BYTE_SIZE - 1) / LONG_BYTE_SIZE; + } + + public static int bytesToLongsKey(byte[] bytes, int byteStart, int byteLength, + long[] longs, int longStart) { + + final int remainder = byteLength % LONG_BYTE_SIZE; + final int wholeLongCount = (byteLength - remainder) / LONG_BYTE_SIZE; + + int longIndex = longStart; + int byteIndex = byteStart; + + // Write length word. Currently, just use the a whole long. + longs[longIndex++] = byteLength; + + // Whole longs. + for (int w = 0; w < wholeLongCount; w++) { + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16 | + ((long) bytes[byteIndex + 3] & 0xffL) << 24 | + ((long) bytes[byteIndex + 4] & 0xffL) << 32 | + ((long) bytes[byteIndex + 5] & 0xffL) << 40 | + ((long) bytes[byteIndex + 6] & 0xffL) << 48 | + ((long) bytes[byteIndex + 7] & 0xffL) << 56; + byteIndex += LONG_BYTE_SIZE; + } + + switch (remainder) { + case 0: + break; + case 1: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL); + break; + case 2: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8; + break; + case 3: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16; + break; + case 4: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16 | + ((long) bytes[byteIndex + 3] & 0xffL) << 24; + break; + case 5: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16 | + ((long) bytes[byteIndex + 3] & 0xffL) << 24 | + ((long) bytes[byteIndex + 4] & 0xffL) << 32; + break; + case 6: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16 | + ((long) bytes[byteIndex + 3] & 0xffL) << 24 | + ((long) bytes[byteIndex + 4] & 0xffL) << 32 | + ((long) bytes[byteIndex + 5] & 0xffL) << 40; + break; + case 7: + longs[longIndex++] = + ((long) bytes[byteIndex] & 0xffL) | + ((long) bytes[byteIndex + 1] & 0xffL) << 8 | + ((long) bytes[byteIndex + 2] & 0xffL) << 16 | + ((long) bytes[byteIndex + 3] & 0xffL) << 24 | + ((long) bytes[byteIndex + 4] & 0xffL) << 32 | + ((long) bytes[byteIndex + 5] & 0xffL) << 40 | + ((long) bytes[byteIndex + 6] & 0xffL) << 48; + break; + } + + return longIndex; + } + + public static int byteLengthFromLongsKey(long[] longs, int longStart) { + // UNDONE: Validation checking + int byteLength = (int) longs[longStart]; + return byteLength; + } + + public static int longsKeyToBytes(long[] longs, int longStart, byte[] bytes, int byteStart) { + + // UNDONE: Validation checking + int byteLength = (int) longs[longStart]; + + final int remainder = byteLength % LONG_BYTE_SIZE; + final int wholeLongCount = (byteLength - remainder) / LONG_BYTE_SIZE; + + int longIndex = longStart + 1; + int byteIndex = byteStart; + + long value; + + // Whole longs. + for (int w = 0; w < wholeLongCount; w++) { + value = longs[longIndex++]; + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + bytes[byteIndex + 3] = (byte) ((value >> 24) & 0xffL); + bytes[byteIndex + 4] = (byte) ((value >> 32) & 0xffL); + bytes[byteIndex + 5] = (byte) ((value >> 40) & 0xffL); + bytes[byteIndex + 6] = (byte) ((value >> 48) & 0xffL); + bytes[byteIndex + 7] = (byte) ((value >> 56) & 0xffL); + byteIndex += LONG_BYTE_SIZE; + } + + if (remainder > 0) { + value = longs[longIndex++]; + switch (remainder) { + case 1: + bytes[byteIndex] = (byte) (value & 0xffL); + break; + case 2: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + break; + case 3: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + break; + case 4: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + bytes[byteIndex + 3] = (byte) ((value >> 24) & 0xffL); + break; + case 5: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + bytes[byteIndex + 3] = (byte) ((value >> 24) & 0xffL); + bytes[byteIndex + 4] = (byte) ((value >> 32) & 0xffL); + bytes[byteIndex + 5] = (byte) ((value >> 40) & 0xffL); + break; + case 6: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + bytes[byteIndex + 3] = (byte) ((value >> 24) & 0xffL); + bytes[byteIndex + 4] = (byte) ((value >> 32) & 0xffL); + bytes[byteIndex + 5] = (byte) ((value >> 40) & 0xffL); + break; + case 7: + bytes[byteIndex] = (byte) (value & 0xffL); + bytes[byteIndex + 1] = (byte) ((value >> 8) & 0xffL); + bytes[byteIndex + 2] = (byte) ((value >> 16) & 0xffL); + bytes[byteIndex + 3] = (byte) ((value >> 24) & 0xffL); + bytes[byteIndex + 4] = (byte) ((value >> 32) & 0xffL); + bytes[byteIndex + 5] = (byte) ((value >> 40) & 0xffL); + bytes[byteIndex + 6] = (byte) ((value >> 48) & 0xffL); + break; + } + } + + return byteLength; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesBytesGroupBy.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesBytesGroupBy.java new file mode 100644 index 0000000..4bddf1e --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesBytesGroupBy.java @@ -0,0 +1,122 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytesBase; +import com.google.common.base.Preconditions; + +/** + * A key series of a single column of byte array keys where the keys get serialized using + * fast SerializeWrite style serialization. + */ +public class VectorKeySeriesBytesGroupBy extends VectorKeySeriesBytesBase + implements VectorKeySeriesGroupBy { + + // The serialized (non-NULL) series keys as longs. + private int serializedLongStart; + private int serializedLongLength; + + private int longOffset; + private int longLength; + + private long[] bytesAsLongs; + + private final int[] serializedKeyLongLengths; + + private static int INIT_BYTES_AS_LONGS_SIZE = 8096; + + public VectorKeySeriesBytesGroupBy(int columnNum) { + super(columnNum); + bytesAsLongs = new long[INIT_BYTES_AS_LONGS_SIZE]; + serializedKeyLongLengths = new int[VectorizedRowBatch.DEFAULT_SIZE]; + } + + @Override + public long[] getSerializedLongs() { + return bytesAsLongs; + } + + @Override + public int getSerializedLongStart() { + return serializedLongStart; + } + + @Override + public int getSerializedLongLength() { + return serializedLongLength; + } + + @Override + public void processBatch(VectorizedRowBatch batch) throws IOException { + longOffset = 0; + + super.processBatch(batch); + + longLength = longOffset; + + Preconditions.checkState( + VectorKeySeriesGroupByUtil.validate(seriesCount, nonNullKeyCount, seriesIsAllNull, + serializedKeyLongLengths, longLength)); + + if (nonNullKeyCount > 0) { + // Compute hash codes of fast serialized keys. + VectorKeySeriesGroupByUtil.computeSerializedHashCodes(bytesAsLongs, serializedKeyLongLengths, + nonNullKeyCount, hashCodes); + } + + // Do the posiiton after we compute the checksums. + positionToFirst(); + } + + @Override + public void saveBytesKey(int nonNullKeyPosition, byte[] keyBytes, int keyByteStart, + int keyByteLength) throws IOException { + if (longOffset + VectorGroupByLongsKey.longsKeyLengthFromByteLength(keyByteLength) >= + bytesAsLongs.length) { + long[] newBytesAsLongs = new long[bytesAsLongs.length * 2]; + System.arraycopy(bytesAsLongs, 0, newBytesAsLongs, 0, longOffset); + bytesAsLongs = newBytesAsLongs; + } + int outputNewOffset = + VectorGroupByLongsKey.bytesToLongsKey(keyBytes, keyByteStart, keyByteLength, + bytesAsLongs, longOffset); + serializedKeyLongLengths[nonNullKeyPosition] = outputNewOffset - longOffset; + longOffset = outputNewOffset; + } + + @Override + public void positionToFirst() { + + // Reset this before calling positionToFirst. + longOffset = 0; + + super.positionToFirst(); + } + + @Override + public void setNextNonNullKey(int nonNullKeyPosition) { + serializedLongStart = longOffset; + serializedLongLength = serializedKeyLongLengths[nonNullKeyPosition]; + Preconditions.checkState(serializedLongStart + serializedLongLength <= longLength); + longOffset += serializedLongLength; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupBy.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupBy.java new file mode 100644 index 0000000..023798c --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupBy.java @@ -0,0 +1,37 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby; + +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeries; + +/** + * An abstract adding to key series the fast SerializeWrite style serialization. + * + * A key with no or some nulls has serialized bytes, offset, and length. + */ +public interface VectorKeySeriesGroupBy extends VectorKeySeries { + + /** + * @return the serialized longs, start offset of the key in the + * longs, and key long length. + */ + long[] getSerializedLongs(); + int getSerializedLongStart(); + int getSerializedLongLength(); +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupByUtil.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupByUtil.java new file mode 100644 index 0000000..d0491fe --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/keyseries/groupby/VectorKeySeriesGroupByUtil.java @@ -0,0 +1,82 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby; + +import org.apache.hadoop.hive.serde2.ByteStream.Output; +import org.apache.hive.common.util.HashCodeUtil; + +import com.google.common.base.Preconditions; + +/** + * Implementation of base fast SerializeWrite style serialization interface. + * + */ +public class VectorKeySeriesGroupByUtil { + + public static boolean validate(int seriesCount, int nonNullKeyCount, boolean[] seriesIsAllNull, + int[] serializedKeyLongLengths, int outputUsedLength) { + + int nullCount = 0; + for (int i = 0; i < seriesCount; i++) { + if (seriesIsAllNull[i]) { + nullCount++; + } + } + Preconditions.checkState(nullCount + nonNullKeyCount == seriesCount); + + validateKeyLengthSum(nonNullKeyCount, serializedKeyLongLengths, outputUsedLength); + + return true; + } + + public static boolean validateKeyLengthSum(int nonNullKeyCount, + int[] serializedKeyLongLengths, int outputUsedLength) { + int longLengthSum = 0; + int keyLongLength; + for (int i = 0; i < nonNullKeyCount; i++) { + keyLongLength = serializedKeyLongLengths[i]; + Preconditions.checkState(keyLongLength > 0); + longLengthSum += keyLongLength; + Preconditions.checkState(longLengthSum <= outputUsedLength); + } + return true; + } + + /** + * Batch compute the hash codes for all the serialized keys. + * + * NOTE: MAJOR MAJOR ASSUMPTION: + * We assume that HashCodeUtil.murmurHash produces the same result + * as MurmurHash.hash with seed = 0 (the method used by ReduceSinkOperator for + * UNIFORM distribution). + */ + protected static void computeSerializedHashCodes(long[] bytesAsLongs, + int[] serializedKeyLongLengths, int nonNullKeyCount, int[] hashCodes) { + int longOffset = 0; + int keyLongLength; + for (int i = 0; i < nonNullKeyCount; i++) { + keyLongLength = serializedKeyLongLengths[i]; + int hashCode = 0; + for (int k = 0; k < keyLongLength; k++) { + hashCode ^= HashCodeUtil.calculateLongHashCode(bytesAsLongs[longOffset++]); + } + hashCodes[i] = hashCode; + } + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinCommonOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinCommonOperator.java index 2502ae2..2722b1f 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinCommonOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinCommonOperator.java @@ -27,10 +27,11 @@ import org.slf4j.LoggerFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.conf.HiveConf; -import org.apache.hadoop.hive.ql.HashTableLoaderFactory; -import org.apache.hadoop.hive.ql.exec.HashTableLoader; import org.apache.hadoop.hive.ql.exec.MapJoinOperator; import org.apache.hadoop.hive.ql.exec.Utilities; +import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorColumnMapping; import org.apache.hadoop.hive.ql.exec.vector.VectorColumnOutputMapping; @@ -42,19 +43,14 @@ import org.apache.hadoop.hive.ql.exec.vector.VectorizationContextRegion; import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.IdentityExpression; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedCreateHashTable; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTable; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast.VectorMapJoinFastHashTableLoader; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.BaseWork; import org.apache.hadoop.hive.ql.plan.ExprNodeDesc; import org.apache.hadoop.hive.ql.plan.MapJoinDesc; import org.apache.hadoop.hive.ql.plan.OperatorDesc; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableImplementationType; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinInfo; import org.apache.hadoop.hive.ql.plan.api.OperatorType; import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinaryDeserializeRead; import org.apache.hadoop.hive.serde2.objectinspector.StructField; @@ -71,7 +67,43 @@ */ public abstract class VectorMapJoinCommonOperator extends MapJoinOperator implements VectorizationContextRegion { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinCommonOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + + private static final String CLASS_NAME = VectorMapJoinCommonOperator.class.getName(); +private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected abstract String getLoggingPrefix(); + + // For debug tracing: information about the map or reduce task, operator, operator class, etc. + protected transient String loggingPrefix; + + protected String getLoggingPrefix(String className) { + if (loggingPrefix == null) { + initLoggingPrefix(className); + } + return loggingPrefix; + } + + protected void initLoggingPrefix(String className) { + if (hconf == null) { + // Constructor time... + loggingPrefix = className; + } else { + // Determine the name of our map or reduce task for debug tracing. + BaseWork work = Utilities.getMapWork(hconf); + if (work == null) { + work = Utilities.getReduceWork(hconf); + } + loggingPrefix = className + " " + work.getName() + " " + getOperatorId(); + } + } + + //------------------------------------------------------------------------------------------------ + + protected VectorMapJoinDesc vectorDesc; + + protected VectorMapJoinInfo vectorMapJoinInfo; // Whether this operator is an outer join. protected boolean isOuterJoin; @@ -87,10 +119,10 @@ // a mixture of input big table columns and new scratch columns. protected VectorizationContext vOutContext; - // The output column projection of the vectorized row batch. And, the type names of the output + // The output column projection of the vectorized row batch. And, the type infos of the output // columns. protected int[] outputProjection; - protected String[] outputTypeNames; + protected TypeInfo[] outputTypeInfos; // These are the vectorized batch expressions for filtering, key expressions, and value // expressions. @@ -100,15 +132,17 @@ // This is map of which vectorized row batch columns are the big table key columns. Since // we may have key expressions that produce new scratch columns, we need a mapping. - // And, we have their type names. + // And, we have their type infos. protected int[] bigTableKeyColumnMap; - protected ArrayList bigTableKeyTypeNames; + protected String[] bigTableKeyColumnNames; + protected TypeInfo[] bigTableKeyTypeInfos; // Similarly, this is map of which vectorized row batch columns are the big table value columns. // Since we may have value expressions that produce new scratch columns, we need a mapping. - // And, we have their type names. + // And, we have their type infos. protected int[] bigTableValueColumnMap; - protected ArrayList bigTableValueTypeNames; + protected String[] bigTableValueColumnNames; + protected TypeInfo[] bigTableValueTypeInfos; // This is a mapping of which big table columns (input and key/value expressions) will be // part of the big table portion of the join output result. @@ -136,9 +170,6 @@ // transient. //--------------------------------------------------------------------------- - // For debug tracing: the name of the map or reduce task. - protected transient String taskName; - // The threshold where we should use a repeating vectorized row batch optimization for // generating join output results. protected transient boolean useOverflowRepeatedThreshold; @@ -174,7 +205,12 @@ protected transient boolean needHashTableSetup; // The small table hash table for the native vectorized map join operator. - protected transient VectorMapJoinHashTable vectorMapJoinHashTable; + protected transient MapJoinHashTableFind vectorMapJoinHashTableFind; + + // The map join hash table factory for create hash table results. + protected transient MapJoinHashTableFactory vectormapJoinHashTableFactory; + + protected transient long totalNumSmallTableKeys; public VectorMapJoinCommonOperator() { super(); @@ -186,6 +222,8 @@ public VectorMapJoinCommonOperator(VectorizationContext vContext, OperatorDesc c MapJoinDesc desc = (MapJoinDesc) conf; this.conf = desc; + vectorDesc = desc.getVectorDesc(); + vectorMapJoinInfo = vectorDesc.getVectorMapJoinInfo(); this.vContext = vContext; @@ -204,47 +242,15 @@ public VectorMapJoinCommonOperator(VectorizationContext vContext, OperatorDesc c bigTableFilterExpressions = vContext.getVectorExpressions(filterExpressions.get(posBigTable), VectorExpressionDescriptor.Mode.FILTER); - List keyDesc = desc.getKeys().get(posBigTable); - bigTableKeyExpressions = vContext.getVectorExpressions(keyDesc); - - // Since a key expression can be a calculation and the key will go into a scratch column, - // we need the mapping and type information. - bigTableKeyColumnMap = new int[bigTableKeyExpressions.length]; - bigTableKeyTypeNames = new ArrayList(); - boolean onlyColumns = true; - for (int i = 0; i < bigTableKeyColumnMap.length; i++) { - VectorExpression ve = bigTableKeyExpressions[i]; - if (!IdentityExpression.isColumnOnly(ve)) { - onlyColumns = false; - } - bigTableKeyTypeNames.add(keyDesc.get(i).getTypeString()); - bigTableKeyColumnMap[i] = ve.getOutputColumn(); - } - if (onlyColumns) { - bigTableKeyExpressions = null; - } - - List bigTableExprs = desc.getExprs().get(posBigTable); - bigTableValueExpressions = vContext.getVectorExpressions(bigTableExprs); + bigTableKeyColumnMap = vectorMapJoinInfo.getBigTableKeyColumnMap(); + bigTableKeyColumnNames = vectorMapJoinInfo.getBigTableKeyColumnNames(); + bigTableKeyTypeInfos = vectorMapJoinInfo.getBigTableKeyTypeInfos(); + bigTableKeyExpressions = vectorMapJoinInfo.getBigTableKeyExpressions(); - /* - * Similarly, we need a mapping since a value expression can be a calculation and the value - * will go into a scratch column. - */ - bigTableValueColumnMap = new int[bigTableValueExpressions.length]; - bigTableValueTypeNames = new ArrayList(); - onlyColumns = true; - for (int i = 0; i < bigTableValueColumnMap.length; i++) { - VectorExpression ve = bigTableValueExpressions[i]; - if (!IdentityExpression.isColumnOnly(ve)) { - onlyColumns = false; - } - bigTableValueTypeNames.add(bigTableExprs.get(i).getTypeString()); - bigTableValueColumnMap[i] = ve.getOutputColumn(); - } - if (onlyColumns) { - bigTableValueExpressions = null; - } + bigTableValueColumnMap = vectorMapJoinInfo.getBigTableValueColumnMap(); + bigTableValueColumnNames = vectorMapJoinInfo.getBigTableValueColumnNames(); + bigTableValueTypeInfos = vectorMapJoinInfo.getBigTableValueTypeInfos(); + bigTableValueExpressions = vectorMapJoinInfo.getBigTableValueExpressions(); determineCommonInfo(isOuterJoin); } @@ -259,14 +265,6 @@ protected void determineCommonInfo(boolean isOuter) { // we use the source ordering flavor for the mapping. smallTableMapping = new VectorColumnSourceMapping("Small Table Mapping"); - // We use a mapping object here so we can build the projection in any order and - // get the ordered by 0 to n-1 output columns at the end. - // - // Also, to avoid copying a big table key into the small table result area for inner joins, - // we reference it with the projection so there can be duplicate output columns - // in the projection. - VectorColumnSourceMapping projectionMapping = new VectorColumnSourceMapping("Projection Mapping"); - /* * Gather up big and small table output result information from the MapJoinDesc. */ @@ -298,6 +296,15 @@ protected void determineCommonInfo(boolean isOuter) { * Determine the big table retained mapping first so we can optimize out (with * projection) copying inner join big table keys in the subsequent small table results section. */ + + // We use a mapping object here so we can build the projection in any order and + // get the ordered by 0 to n-1 output columns at the end. + // + // Also, to avoid copying a big table key into the small table result area for inner joins, + // we reference it with the projection so there can be duplicate output columns + // in the projection. + VectorColumnSourceMapping projectionMapping = new VectorColumnSourceMapping("Projection Mapping"); + int nextOutputColumn = (order[0] == posBigTable ? 0 : smallTableResultSize); for (int i = 0; i < bigTableRetainSize; i++) { @@ -306,15 +313,15 @@ protected void determineCommonInfo(boolean isOuter) { int retainColumn = bigTableRetainList.get(i); int batchColumnIndex = bigTableValueColumnMap[retainColumn]; - String typeName = bigTableValueTypeNames.get(i); + TypeInfo typeInfo = bigTableValueTypeInfos[i]; // With this map we project the big table batch to make it look like an output batch. - projectionMapping.add(nextOutputColumn, batchColumnIndex, typeName); + projectionMapping.add(nextOutputColumn, batchColumnIndex, typeInfo); // Collect columns we copy from the big table batch to the overflow batch. if (!bigTableRetainedMapping.containsOutputColumn(batchColumnIndex)) { // Tolerate repeated use of a big table column. - bigTableRetainedMapping.add(batchColumnIndex, batchColumnIndex, typeName); + bigTableRetainedMapping.add(batchColumnIndex, batchColumnIndex, typeInfo); } nextOutputColumn++; @@ -329,8 +336,10 @@ protected void determineCommonInfo(boolean isOuter) { nextOutputColumn = firstSmallTableOutputColumn; // Small table indices has more information (i.e. keys) than retain, so use it if it exists... + String[] bigTableRetainedNames; if (smallTableIndicesSize > 0) { smallTableOutputCount = smallTableIndicesSize; + bigTableRetainedNames = new String[smallTableOutputCount]; for (int i = 0; i < smallTableIndicesSize; i++) { if (smallTableIndices[i] >= 0) { @@ -343,19 +352,20 @@ protected void determineCommonInfo(boolean isOuter) { // Since bigTableKeyExpressions may do a calculation and produce a scratch column, we // need to map the right column. int batchKeyColumn = bigTableKeyColumnMap[keyIndex]; - String typeName = bigTableKeyTypeNames.get(keyIndex); + bigTableRetainedNames[i] = bigTableKeyColumnNames[keyIndex]; + TypeInfo typeInfo = bigTableKeyTypeInfos[keyIndex]; if (!isOuter) { // Optimize inner join keys of small table results. // Project the big table key into the small table result "area". - projectionMapping.add(nextOutputColumn, batchKeyColumn, typeName); + projectionMapping.add(nextOutputColumn, batchKeyColumn, typeInfo); if (!bigTableRetainedMapping.containsOutputColumn(batchKeyColumn)) { // If necessary, copy the big table key into the overflow batch's small table // result "area". - bigTableRetainedMapping.add(batchKeyColumn, batchKeyColumn, typeName); + bigTableRetainedMapping.add(batchKeyColumn, batchKeyColumn, typeInfo); } } else { @@ -363,12 +373,12 @@ protected void determineCommonInfo(boolean isOuter) { // we must have a physical (scratch) column for those keys. We cannot use the // projection optimization used by inner joins above. - int scratchColumn = vOutContext.allocateScratchColumn(typeName); - projectionMapping.add(nextOutputColumn, scratchColumn, typeName); + int scratchColumn = vOutContext.allocateScratchColumn(typeInfo.getTypeName()); + projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo); - bigTableRetainedMapping.add(batchKeyColumn, scratchColumn, typeName); + bigTableRetainedMapping.add(batchKeyColumn, scratchColumn, typeInfo); - bigTableOuterKeyMapping.add(batchKeyColumn, scratchColumn, typeName); + bigTableOuterKeyMapping.add(batchKeyColumn, scratchColumn, typeInfo); } } else { @@ -376,33 +386,44 @@ protected void determineCommonInfo(boolean isOuter) { // LazyBinary value row. int smallTableValueIndex = -smallTableIndices[i] - 1; - String typeName = smallTableExprs.get(i).getTypeString(); + ExprNodeDesc smallTableExprNode = smallTableExprs.get(i); + + bigTableRetainedNames[i] = smallTableExprNode.toString(); + + TypeInfo typeInfo = smallTableExprNode.getTypeInfo(); // Make a new big table scratch column for the small table value. - int scratchColumn = vOutContext.allocateScratchColumn(typeName); - projectionMapping.add(nextOutputColumn, scratchColumn, typeName); + int scratchColumn = vOutContext.allocateScratchColumn(typeInfo.getTypeName()); + projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo); - smallTableMapping.add(smallTableValueIndex, scratchColumn, typeName); + smallTableMapping.add(smallTableValueIndex, scratchColumn, typeInfo); } nextOutputColumn++; } } else if (smallTableRetainSize > 0) { smallTableOutputCount = smallTableRetainSize; + bigTableRetainedNames = new String[smallTableOutputCount]; // Only small table values appear in join output result. for (int i = 0; i < smallTableRetainSize; i++) { int smallTableValueIndex = smallTableRetainList.get(i); + ExprNodeDesc smallTableExprNode = smallTableExprs.get(i); + + bigTableRetainedNames[i] = smallTableExprNode.toString(); + // Make a new big table scratch column for the small table value. - String typeName = smallTableExprs.get(i).getTypeString(); - int scratchColumn = vOutContext.allocateScratchColumn(typeName); + TypeInfo typeInfo = smallTableExprNode.getTypeInfo(); + int scratchColumn = vOutContext.allocateScratchColumn(typeInfo.getTypeName()); - projectionMapping.add(nextOutputColumn, scratchColumn, typeName); + projectionMapping.add(nextOutputColumn, scratchColumn, typeInfo); - smallTableMapping.add(smallTableValueIndex, scratchColumn, typeName); + smallTableMapping.add(smallTableValueIndex, scratchColumn, typeInfo); nextOutputColumn++; } + } else { + bigTableRetainedNames = new String[0]; } // Convert dynamic arrays and maps to simple arrays. @@ -429,40 +450,44 @@ protected void determineCommonInfo(boolean isOuter) { assert projectionMapping.isSourceSequenceGood(); outputProjection = projectionMapping.getOutputColumns(); - outputTypeNames = projectionMapping.getTypeNames(); + outputTypeInfos = projectionMapping.getTypeInfos(); if (isLogDebugEnabled) { int[] orderDisplayable = new int[order.length]; for (int i = 0; i < order.length; i++) { orderDisplayable[i] = (int) order[i]; } - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor order " + Arrays.toString(orderDisplayable)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor posBigTable " + (int) posBigTable); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor posSingleVectorMapJoinSmallTable " + (int) posSingleVectorMapJoinSmallTable); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor order " + Arrays.toString(orderDisplayable)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor posBigTable " + (int) posBigTable); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor posSingleVectorMapJoinSmallTable " + (int) posSingleVectorMapJoinSmallTable); + + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableKeyColumnMap " + Arrays.toString(bigTableKeyColumnMap)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableKeyColumnNames " + Arrays.toString(bigTableKeyColumnNames)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableKeyTypeInfos " + Arrays.toString(bigTableKeyTypeInfos)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableKeyColumnMap " + Arrays.toString(bigTableKeyColumnMap)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableKeyTypeNames " + bigTableKeyTypeNames); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableValueColumnMap " + Arrays.toString(bigTableValueColumnMap)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableValueColumnNames " + Arrays.toString(bigTableValueColumnNames)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableValueTypeNames " + Arrays.toString(bigTableValueTypeInfos)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableValueColumnMap " + Arrays.toString(bigTableValueColumnMap)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableValueTypeNames " + bigTableValueTypeNames); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor smallTableIndices " + Arrays.toString(smallTableIndices)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor smallTableRetainList " + smallTableRetainList); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor smallTableIndices " + Arrays.toString(smallTableIndices)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor smallTableRetainList " + smallTableRetainList); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor firstSmallTableOutputColumn " + firstSmallTableOutputColumn); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor smallTableOutputCount " + smallTableOutputCount); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor firstSmallTableOutputColumn " + firstSmallTableOutputColumn); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor smallTableOutputCount " + smallTableOutputCount); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableRetainedNames " + Arrays.toString(bigTableRetainedNames)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableRetainedMapping " + bigTableRetainedMapping.toString()); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableRetainedMapping " + bigTableRetainedMapping.toString()); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableOuterKeyMapping " + bigTableOuterKeyMapping.toString()); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableOuterKeyMapping " + bigTableOuterKeyMapping.toString()); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor smallTableMapping " + smallTableMapping.toString()); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor smallTableMapping " + smallTableMapping.toString()); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor bigTableByteColumnVectorColumns " + Arrays.toString(bigTableByteColumnVectorColumns)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor smallTableByteColumnVectorColumns " + Arrays.toString(smallTableByteColumnVectorColumns)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor bigTableByteColumnVectorColumns " + Arrays.toString(bigTableByteColumnVectorColumns)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor smallTableByteColumnVectorColumns " + Arrays.toString(smallTableByteColumnVectorColumns)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor outputProjection " + Arrays.toString(outputProjection)); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor outputTypeNames " + Arrays.toString(outputTypeNames)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor outputProjection " + Arrays.toString(outputProjection)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor outputTypeInfos " + Arrays.toString(outputTypeInfos)); } setupVOutContext(conf.getOutputColumnNames()); @@ -476,10 +501,10 @@ protected void determineCommonInfo(boolean isOuter) { ArrayList list = new ArrayList(); int count = mapping.getCount(); int[] outputColumns = mapping.getOutputColumns(); - String[] typeNames = mapping.getTypeNames(); + TypeInfo[] typeInfos = mapping.getTypeInfos(); for (int i = 0; i < count; i++) { int outputColumn = outputColumns[i]; - String typeName = typeNames[i]; + String typeName = typeInfos[i].getTypeName(); if (VectorizationContext.isStringFamily(typeName)) { list.add(outputColumn); } @@ -494,10 +519,10 @@ protected void determineCommonInfo(boolean isOuter) { */ protected void setupVOutContext(List outputColumnNames) { if (isLogDebugEnabled) { - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor outputColumnNames " + outputColumnNames); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor outputColumnNames " + outputColumnNames); } if (outputColumnNames.size() != outputProjection.length) { - throw new RuntimeException("Output column names " + outputColumnNames + " length and output projection " + Arrays.toString(outputProjection) + " / " + Arrays.toString(outputTypeNames) + " length mismatch"); + throw new RuntimeException("Output column names " + outputColumnNames + " length and output projection " + Arrays.toString(outputProjection) + " / " + Arrays.toString(outputTypeInfos) + " length mismatch"); } vOutContext.resetProjectionColumns(); for (int i = 0; i < outputColumnNames.size(); ++i) { @@ -506,49 +531,15 @@ protected void setupVOutContext(List outputColumnNames) { vOutContext.addProjectionColumn(columnName, outputColumn); if (isLogDebugEnabled) { - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator constructor addProjectionColumn " + i + " columnName " + columnName + " outputColumn " + outputColumn); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator constructor addProjectionColumn " + i + " columnName " + columnName + " outputColumn " + outputColumn); } } } - /** - * This override lets us substitute our own fast vectorized hash table loader. - */ - @Override - protected HashTableLoader getHashTableLoader(Configuration hconf) { - VectorMapJoinDesc vectorDesc = conf.getVectorDesc(); - HashTableImplementationType hashTableImplementationType = vectorDesc.hashTableImplementationType(); - HashTableLoader hashTableLoader; - switch (vectorDesc.hashTableImplementationType()) { - case OPTIMIZED: - // Use the Tez hash table loader. - hashTableLoader = HashTableLoaderFactory.getLoader(hconf); - break; - case FAST: - // Use our specialized hash table loader. - hashTableLoader = HiveConf.getVar( - hconf, HiveConf.ConfVars.HIVE_EXECUTION_ENGINE).equals("spark") ? - HashTableLoaderFactory.getLoader(hconf) : new VectorMapJoinFastHashTableLoader(); - break; - default: - throw new RuntimeException("Unknown vector map join hash table implementation type " + hashTableImplementationType.name()); - } - return hashTableLoader; - } - @Override protected void initializeOp(Configuration hconf) throws HiveException { super.initializeOp(hconf); - if (isLogDebugEnabled) { - // Determine the name of our map or reduce task for debug tracing. - BaseWork work = Utilities.getMapWork(hconf); - if (work == null) { - work = Utilities.getReduceWork(hconf); - } - taskName = work.getName(); - } - /* * Get configuration parameters. */ @@ -564,8 +555,7 @@ protected void initializeOp(Configuration hconf) throws HiveException { smallTableVectorDeserializeRow = new VectorDeserializeRow( new LazyBinaryDeserializeRead( - VectorizedBatchUtil.typeInfosFromTypeNames( - smallTableMapping.getTypeNames()))); + smallTableMapping.getTypeInfos())); smallTableVectorDeserializeRow.init(smallTableMapping.getOutputColumns()); } @@ -589,13 +579,13 @@ protected void initializeOp(Configuration hconf) throws HiveException { if (isLogDebugEnabled) { int[] currentScratchColumns = vOutContext.currentScratchColumns(); - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator initializeOp currentScratchColumns " + Arrays.toString(currentScratchColumns)); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator initializeOp currentScratchColumns " + Arrays.toString(currentScratchColumns)); StructObjectInspector structOutputObjectInspector = (StructObjectInspector) outputObjInspector; List fields = structOutputObjectInspector.getAllStructFieldRefs(); int i = 0; for (StructField field : fields) { - LOG.debug("VectorMapJoinInnerBigOnlyCommonOperator initializeOp " + i + " field " + field.getFieldName() + " type " + field.getFieldObjectInspector().getTypeName()); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator initializeOp " + i + " field " + field.getFieldName() + " type " + field.getFieldObjectInspector().getTypeName()); i++; } } @@ -606,30 +596,16 @@ protected void completeInitializationOp(Object[] os) throws HiveException { // setup mapJoinTables and serdes super.completeInitializationOp(os); - VectorMapJoinDesc vectorDesc = conf.getVectorDesc(); - HashTableImplementationType hashTableImplementationType = vectorDesc.hashTableImplementationType(); - switch (vectorDesc.hashTableImplementationType()) { - case OPTIMIZED: - { - // Create our vector map join optimized hash table variation *above* the - // map join table container. - vectorMapJoinHashTable = VectorMapJoinOptimizedCreateHashTable.createHashTable(conf, - mapJoinTables[posSingleVectorMapJoinSmallTable]); - } - break; - - case FAST: - { - // Get our vector map join fast hash table variation from the - // vector map join table container. - VectorMapJoinTableContainer vectorMapJoinTableContainer = - (VectorMapJoinTableContainer) mapJoinTables[posSingleVectorMapJoinSmallTable]; - vectorMapJoinHashTable = vectorMapJoinTableContainer.vectorMapJoinHashTable(); - } - break; - default: - throw new RuntimeException("Unknown vector map join hash table implementation type " + hashTableImplementationType.name()); - } + MapJoinTableContainer mapJoinTableContainer = + mapJoinTables[posSingleVectorMapJoinSmallTable]; + + // The hash table for the specialized operator. + vectorMapJoinHashTableFind = mapJoinTableContainer.getMapJoinHashTableFind(); + + // The factory so we can create result objects. + vectormapJoinHashTableFactory = mapJoinTableContainer.getMapJoinHashTableFactory(); + + totalNumSmallTableKeys = mapJoinTableContainer.size(); } /* @@ -647,7 +623,7 @@ protected VectorizedRowBatch setupOverflowBatch() throws HiveException { // First, just allocate just the projection columns we will be using. for (int i = 0; i < outputProjection.length; i++) { int outputColumn = outputProjection[i]; - String typeName = outputTypeNames[i]; + String typeName = outputTypeInfos[i].getTypeName(); allocateOverflowBatchColumnVector(overflowBatch, outputColumn, typeName); } @@ -679,7 +655,7 @@ private void allocateOverflowBatchColumnVector(VectorizedRowBatch overflowBatch, overflowBatch.cols[outputColumn] = VectorizedBatchUtil.createColumnVector(typeInfo); if (isLogDebugEnabled) { - LOG.debug(taskName + ", " + getOperatorId() + " VectorMapJoinCommonOperator initializeOp overflowBatch outputColumn " + outputColumn + " class " + overflowBatch.cols[outputColumn].getClass().getSimpleName()); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator initializeOp overflowBatch outputColumn " + outputColumn + " class " + overflowBatch.cols[outputColumn].getClass().getSimpleName()); } } } @@ -716,9 +692,9 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { } protected void displayBatchColumns(VectorizedRowBatch batch, String batchName) { - LOG.debug("commonSetup " + batchName + " column count " + batch.numCols); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator commonSetup " + batchName + " column count " + batch.numCols); for (int column = 0; column < batch.numCols; column++) { - LOG.debug("commonSetup " + batchName + " column " + column + " type " + (batch.cols[column] == null ? "NULL" : batch.cols[column].getClass().getSimpleName())); + LOG.debug(getLoggingPrefix() + " VectorMapJoinCommonOperator commonSetup " + batchName + " column " + column + " type " + (batch.cols[column] == null ? "NULL" : batch.cols[column].getClass().getSimpleName())); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java index c1c137b..5a18e5c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java @@ -20,15 +20,18 @@ import java.io.IOException; import java.util.ArrayList; +import java.util.Arrays; import java.util.List; import org.apache.commons.lang.ArrayUtils; + import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer; import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer.HashPartition; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer; import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorDeserializeRow; import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; @@ -36,9 +39,6 @@ import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedCreateHashTable; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; import org.apache.hadoop.hive.serde2.SerDeException; @@ -46,11 +46,11 @@ import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinaryDeserializeRead; import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinarySerializeWrite; import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector; -import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; -import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils; import org.apache.hadoop.hive.serde2.ByteStream.Output; +import com.google.common.base.Preconditions; + /** * This class has methods for generating vectorized join results and forwarding batchs. * @@ -72,8 +72,16 @@ public abstract class VectorMapJoinGenerateResultOperator extends VectorMapJoinCommonOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinGenerateResultOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinGenerateResultOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix(String className) { + // Use operator's class name. + return super.getLoggingPrefix(className); + } //------------------------------------------------------------------------------------------------ @@ -85,6 +93,13 @@ // Debug display. protected transient long batchCounter; + protected transient long spilledRowCounter; + protected transient long inputRowCounter; + protected transient long bigTableOutputRowCounter; + protected transient long overflowOutputRowCounter; + + protected transient long singleValueCounter; + protected transient long multiValueCounter; public VectorMapJoinGenerateResultOperator() { super(); @@ -99,13 +114,138 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { super.commonSetup(batch); batchCounter = 0; + spilledRowCounter = 0; + inputRowCounter = 0; + bigTableOutputRowCounter = 0; + overflowOutputRowCounter = 0; + + singleValueCounter = 0; + multiValueCounter = 0; + } + + //------------------------------------------------------------------------------------------------ + + protected static int makeSelectedByRemovingSeries( + boolean selectedInUse, int[] selected, int selectedSize, + int[] seriesLogicalIndices, int[] seriesDuplicateCounts, int seriesCount, + boolean seriesSelectedInUse, int[] seriesSelected, int seriesSelectedSize, + int[] resultSelected) { + int resultCount = 0; + int logical = 0; + for (int i = 0; i < seriesCount; i++) { + int batchIndex = (selectedInUse ? selected[logical] : logical); + + int seriesLogical = seriesLogicalIndices[i]; + int seriesBatchIndex = (seriesSelectedInUse ? seriesSelected[seriesLogical] : seriesLogical); + + // Add any selected batch indices before series batch index; + while (batchIndex < seriesBatchIndex) { + resultSelected[resultCount++] = batchIndex; + logical++; + batchIndex = (selectedInUse ? selected[logical] : logical); + } + Preconditions.checkState(batchIndex == seriesBatchIndex); + + // Skip series + logical += seriesDuplicateCounts[i]; + } + + // Grab non series after last series. + while (logical < selectedSize) { + int batchIndex = (selectedInUse ? selected[logical] : logical); + resultSelected[resultCount++] = batchIndex; + logical++; + } + return resultCount; + } + + protected static int makeSelectedByRemovingMultiValues( + boolean selectedInUse, int[] selected, int selectedSize, + int[] matchLogicalIndices, int[] matchDuplicateCounts, boolean[] matchIsSingleValue, + int matchSeriesCount, + boolean seriesSelectedInUse, int[] seriesSelected, int seriesSelectedSize, + int[] resultSelected) { + int resultCount = 0; + int logical = 0; + for (int i = 0; i < matchSeriesCount; i++) { + if (matchIsSingleValue[i]) { + continue; + } + int batchIndex = (selectedInUse ? selected[logical] : logical); + + int seriesLogical = matchLogicalIndices[i]; + int seriesBatchIndex = (seriesSelectedInUse ? seriesSelected[seriesLogical] : seriesLogical); + + // Add any selected batch indices before match series multi-value batch index, including + // match single-value series. + while (batchIndex < seriesBatchIndex) { + resultSelected[resultCount++] = batchIndex; + logical++; + batchIndex = (selectedInUse ? selected[logical] : logical); + } + Preconditions.checkState(batchIndex == seriesBatchIndex); + + // Skip series + logical += matchDuplicateCounts[i]; + } + + // Grab non series after last match series multi-value. + while (logical < selectedSize) { + int batchIndex = (selectedInUse ? selected[logical] : logical); + resultSelected[resultCount++] = batchIndex; + logical++; + } + return resultCount; + } + + + protected static int flattenLogicalSeriesIntoSelected( + boolean selectedInUse, int[] selected, int size, + int[] seriesLogicalIndices, int[] seriesDuplicateCounts, int seriesCount, + int[] resultSelected) { + + int resultCount = 0; + for (int i = 0; i < seriesCount; i++) { + int seriesLogical = seriesLogicalIndices[i]; + int seriesDuplicateCount = seriesDuplicateCounts[i]; + for (int s = seriesLogical; s < seriesLogical + seriesDuplicateCount; s++) { + int batchIndex = (selectedInUse ? selected[s] : s); + resultSelected[resultCount++] = batchIndex; + } + } + return resultCount; + } + + protected static int makeMatchSelectedWithoutMultiValues( + boolean selectedInUse, int[] selected, int size, + int[] matchLogicalIndices, int[] matchDuplicateCounts, boolean[] matchIsSingleValue, + int matchSeriesCount, + int[] resultSelected) { + + int resultCount = 0; + int matchLogical; + int matchDuplicateCount; + for (int i = 0; i < matchSeriesCount; i++) { + if (matchIsSingleValue[i]) { + // Only include single-value small table result series. + matchLogical = matchLogicalIndices[i]; + matchDuplicateCount = matchDuplicateCounts[i]; + for (int m = matchLogical; m < matchLogical + matchDuplicateCount; m++) { + int batchIndex = (selectedInUse ? selected[m] : m); + resultSelected[resultCount++] = batchIndex; + } + } + } + + return resultCount; } //------------------------------------------------------------------------------------------------ - protected void performValueExpressions(VectorizedRowBatch batch, - int[] allMatchs, int allMatchCount) { + protected void performValueExpressions(VectorizedRowBatch batch, int[] newSelected, + int newSelectedCount) { + /* * For the moment, pretend all matched are selected so we can evaluate the value * expressions. @@ -114,10 +254,10 @@ protected void performValueExpressions(VectorizedRowBatch batch, * selected and real batch size later... */ int[] saveSelected = batch.selected; - batch.selected = allMatchs; + batch.selected = newSelected; boolean saveSelectedInUse = batch.selectedInUse; batch.selectedInUse = true; - batch.size = allMatchCount; + batch.size = newSelectedCount; // Run our value expressions over whole batch. for(VectorExpression ve: bigTableValueExpressions) { @@ -152,9 +292,13 @@ protected void performValueExpressions(VectorizedRowBatch batch, * @return * The new count of selected rows. */ - protected int generateHashMapResultSingleValue(VectorizedRowBatch batch, - VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, - int duplicateCount, int numSel) throws HiveException, IOException { + protected void generateHashMapResultSingleValue(VectorizedRowBatch batch, + MapJoinHashMapResult hashMapResult, int logical, int duplicateCount, + boolean selectedInUse, int[] selected, int size) throws HiveException, IOException { + + singleValueCounter += duplicateCount; + + // LOG.info(getLoggingPrefix() + " generateHashMapResultSingleValue entering... logical " + logical + " duplicateCount " + duplicateCount + "hashMapResult " + hashMapResult.toString()); // Read single value. @@ -162,9 +306,11 @@ protected int generateHashMapResultSingleValue(VectorizedRowBatch batch, // Generate result within big table batch itself. - for (int i = 0; i < duplicateCount; i++) { + for (int i = logical; i < logical + duplicateCount; i++) { + + int batchIndex = (selectedInUse ? selected[i] : i); - int batchIndex = allMatchs[allMatchesIndex + i]; + // LOG.info(getLoggingPrefix() + " generateHashMapResultSingleValue logical " + logical + " batchIndex " + batchIndex); // Outer key copying is only used when we are using the input BigTable batch as the output. // @@ -183,13 +329,8 @@ protected int generateHashMapResultSingleValue(VectorizedRowBatch batch, smallTableVectorDeserializeRow.deserializeByValue(batch, batchIndex); } - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, "generateHashMapResultSingleValue big table"); - - // Use the big table row as output. - batch.selected[numSel++] = batchIndex; + // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, getLoggingPrefix() + " generateHashMapResultSingleValue"); } - - return numSel; } /** @@ -207,8 +348,10 @@ protected int generateHashMapResultSingleValue(VectorizedRowBatch batch, * Number of equal key rows. */ protected void generateHashMapResultMultiValue(VectorizedRowBatch batch, - VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, - int duplicateCount) throws HiveException, IOException { + MapJoinHashMapResult hashMapResult, int logical, int duplicateCount, + boolean selectedInUse, int[] selected, int size) throws HiveException, IOException { + + multiValueCounter += duplicateCount; if (useOverflowRepeatedThreshold && hashMapResult.isCappedCountAvailable() && @@ -218,16 +361,16 @@ protected void generateHashMapResultMultiValue(VectorizedRowBatch batch, // row batch optimization in the overflow batch. generateHashMapResultLargeMultiValue( - batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount); + batch, hashMapResult, logical, duplicateCount, selectedInUse, selected, size); return; } // We do the cross product of the N big table equal key row's values against the // small table matching key which has M value rows into overflow batch. - for (int i = 0; i < duplicateCount; i++) { + for (int i = logical; i < logical + duplicateCount; i++) { - int batchIndex = allMatchs[allMatchesIndex + i]; + int batchIndex = (selectedInUse ? selected[i] : i); ByteSegmentRef byteSegmentRef = hashMapResult.first(); while (byteSegmentRef != null) { @@ -277,8 +420,8 @@ protected void generateHashMapResultMultiValue(VectorizedRowBatch batch, * Number of equal key rows. */ private void generateHashMapResultLargeMultiValue(VectorizedRowBatch batch, - VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, - int duplicateCount) throws HiveException, IOException { + MapJoinHashMapResult hashMapResult, int logical, int duplicateCount, + boolean selectedInUse, int[] selected, int size) throws HiveException, IOException { // Kick out previous overflow batch results. if (overflowBatch.size > 0) { @@ -319,9 +462,9 @@ private void generateHashMapResultLargeMultiValue(VectorizedRowBatch batch, // And, not set repeating every time... // - for (int i = 0; i < duplicateCount; i++) { + for (int i = logical; i < logical + duplicateCount; i++) { - int batchIndex = allMatchs[allMatchesIndex + i]; + int batchIndex = (selectedInUse ? selected[i] : i); if (bigTableRetainedVectorCopy != null) { // The one big table row's values repeat. @@ -354,41 +497,6 @@ private void generateHashMapResultLargeMultiValue(VectorizedRowBatch batch, overflowBatch.reset(); } - /** - * Generate optimized results when entire batch key is repeated and it matched the hash map. - * - * @param batch - * The big table batch. - * @param hashMapResult - * The hash map results for the repeated key. - */ - protected void generateHashMapResultRepeatedAll(VectorizedRowBatch batch, - VectorMapJoinHashMapResult hashMapResult) throws IOException, HiveException { - - int[] selected = batch.selected; - - if (batch.selectedInUse) { - // The selected array is already filled in as we want it. - } else { - for (int i = 0; i < batch.size; i++) { - selected[i] = i; - } - batch.selectedInUse = true; - } - - int numSel = 0; - if (hashMapResult.isSingleRow()) { - numSel = generateHashMapResultSingleValue(batch, hashMapResult, - batch.selected, 0, batch.size, numSel); - - } else { - generateHashMapResultMultiValue(batch, hashMapResult, - batch.selected, 0, batch.size); - } - - batch.size = numSel; - } - //----------------------------------------------------------------------------------------------- /* @@ -435,9 +543,9 @@ private void setupSpillSerDe(VectorizedRowBatch batch) throws HiveException { } private void spillSerializeRow(VectorizedRowBatch batch, int batchIndex, - VectorMapJoinHashTableResult hashTableResult) throws IOException { + MapJoinHashTableResult hashTableResult) throws IOException { - int partitionId = hashTableResult.spillPartitionId(); + int partitionId = hashTableResult.getSpillPartitionId(); HybridHashTableContainer ht = (HybridHashTableContainer) mapJoinTables[posSingleVectorMapJoinSmallTable]; HashPartition hp = ht.getHashPartitions()[partitionId]; @@ -453,9 +561,9 @@ private void spillSerializeRow(VectorizedRowBatch batch, int batchIndex, // LOG.debug("spillSerializeRow spilled batchIndex " + batchIndex + ", length " + length); } - protected void spillHashMapBatch(VectorizedRowBatch batch, - VectorMapJoinHashTableResult[] hashTableResults, - int[] spills, int[] spillHashTableResultIndices, int spillCount) + protected void spillHashMapBatch(VectorizedRowBatch batch, int[] spillLogicalIndices, + int[] spillDuplicateCounts, MapJoinHashTableResult[] spillHashTableResults, + int spillCount, boolean selectedInUse, int[] selected, int size) throws HiveException, IOException { if (bigTableVectorSerializeRow == null) { @@ -463,27 +571,31 @@ protected void spillHashMapBatch(VectorizedRowBatch batch, } for (int i = 0; i < spillCount; i++) { - int batchIndex = spills[i]; - - int hashTableResultIndex = spillHashTableResultIndices[i]; - VectorMapJoinHashTableResult hashTableResult = hashTableResults[hashTableResultIndex]; - - spillSerializeRow(batch, batchIndex, hashTableResult); + int logical = spillLogicalIndices[i]; + int duplicateCount = spillDuplicateCounts[i]; + MapJoinHashTableResult hashTableResult = spillHashTableResults[i]; + for (int s = logical; s < logical + duplicateCount; s++) { + int batchIndex = (selectedInUse ? selected[s] : s); + spillSerializeRow(batch, batchIndex, hashTableResult); + } } } - protected void spillBatchRepeated(VectorizedRowBatch batch, - VectorMapJoinHashTableResult hashTableResult) throws HiveException, IOException { + protected void spillHashMapBatch(VectorizedRowBatch batch, + MapJoinHashTableResult[] hashTableResults, + int[] spills, int[] spillHashTableResultIndices, int spillCount) + throws HiveException, IOException { if (bigTableVectorSerializeRow == null) { setupSpillSerDe(batch); } - int[] selected = batch.selected; - boolean selectedInUse = batch.selectedInUse; + for (int i = 0; i < spillCount; i++) { + int batchIndex = spills[i]; + + int hashTableResultIndex = spillHashTableResultIndices[i]; + MapJoinHashTableResult hashTableResult = hashTableResults[hashTableResultIndex]; - for (int logical = 0; logical < batch.size; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); spillSerializeRow(batch, batchIndex, hashTableResult); } } @@ -498,8 +610,8 @@ protected void reloadHashTable(byte pos, int partitionId) MapJoinTableContainer smallTable = spilledMapJoinTables[pos]; - vectorMapJoinHashTable = VectorMapJoinOptimizedCreateHashTable.createHashTable(conf, - smallTable); + vectorMapJoinHashTableFind = smallTable.getMapJoinHashTableFind(); + needHashTableSetup = true; if (isLogDebugEnabled) { @@ -590,7 +702,17 @@ public void forwardBigTableBatch(VectorizedRowBatch batch) throws HiveException batch.projectionSize = outputProjection.length; batch.projectedColumns = outputProjection; + /* + if (batch.selectedInUse) { + LOG.info(getLoggingPrefix() + " forward selected " + intArrayToRangesString(batch.selected, batch.size)); + } + LOG.info(getLoggingPrefix() + " forward projected " + Arrays.toString(batch.projectedColumns)); + */ + + VectorizedBatchUtil.debugDisplayBatch(batch, CLASS_NAME); + forward(batch, null); + bigTableOutputRowCounter += batch.size; // Revert the projected columns back, because batch can be re-used by our parent operators. batch.projectionSize = originalProjectionSize; @@ -603,6 +725,7 @@ public void forwardBigTableBatch(VectorizedRowBatch batch) throws HiveException */ protected void forwardOverflow() throws HiveException { forward(overflowBatch, null); + overflowOutputRowCounter += overflowBatch.size; overflowBatch.reset(); } @@ -611,6 +734,7 @@ protected void forwardOverflow() throws HiveException { */ private void forwardOverflowNoReset() throws HiveException { forward(overflowBatch, null); + overflowOutputRowCounter += overflowBatch.size; } /* @@ -623,11 +747,20 @@ private void forwardOverflowNoReset() throws HiveException { @Override public void closeOp(boolean aborted) throws HiveException { super.closeOp(aborted); - if (!aborted && overflowBatch.size > 0) { - forwardOverflow(); - } - if (isLogDebugEnabled) { - LOG.debug("VectorMapJoinInnerLongOperator closeOp " + batchCounter + " batches processed"); + if (!aborted) { + if (overflowBatch.size > 0) { + forwardOverflow(); + } + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " closeOp " + batchCounter + " batches processed, " + + inputRowCounter + " big table input rows, " + + totalNumSmallTableKeys + " small table keys, " + + spilledRowCounter + " spilled rows, " + + singleValueCounter + " single value rows, " + + multiValueCounter + " multiple value rows " + + bigTableOutputRowCounter + " big table output rows, " + + overflowOutputRowCounter + " overflow output rows"); + } } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.orig ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.orig new file mode 100644 index 0000000..c1c137b --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.orig @@ -0,0 +1,842 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.mapjoin; + +import java.io.IOException; +import java.util.ArrayList; +import java.util.List; + +import org.apache.commons.lang.ArrayUtils; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer; +import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer.HashPartition; +import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer; +import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorDeserializeRow; +import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; +import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedCreateHashTable; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; +import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinaryDeserializeRead; +import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinarySerializeWrite; +import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils; +import org.apache.hadoop.hive.serde2.ByteStream.Output; + +/** + * This class has methods for generating vectorized join results and forwarding batchs. + * + * In some cases when can forward the big table batch by setting scratch columns + * with small table results and then making use of our output projection to pick out all the + * output result columns. This can improve performance by avoiding copying big table values. + * So, we will use the big table batch's selected in use to represent those rows. + * + * At the same time, some output results need to be formed in the overflow batch. + * For example, to form N x M cross product output results. In this case, we will copy big + * table values into the overflow batch and set scratch columns in it for small table results. + * The "schema" of the overflow batch is the same as the big table batch so child operators + * only need one definition of their input batch. The overflow batch will be typically be + * forwarded when it gets full, which might not be during a process call. + * + * NOTE: Child operators should not remember a received batch. + */ + +public abstract class VectorMapJoinGenerateResultOperator extends VectorMapJoinCommonOperator { + + private static final long serialVersionUID = 1L; + private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinGenerateResultOperator.class.getName()); + private static final String CLASS_NAME = VectorMapJoinGenerateResultOperator.class.getName(); + + //------------------------------------------------------------------------------------------------ + + private transient TypeInfo[] bigTableTypeInfos; + + private transient VectorSerializeRow bigTableVectorSerializeRow; + + private transient VectorDeserializeRow bigTableVectorDeserializeRow; + + // Debug display. + protected transient long batchCounter; + + public VectorMapJoinGenerateResultOperator() { + super(); + } + + public VectorMapJoinGenerateResultOperator(VectorizationContext vContext, OperatorDesc conf) + throws HiveException { + super(vContext, conf); + } + + protected void commonSetup(VectorizedRowBatch batch) throws HiveException { + super.commonSetup(batch); + + batchCounter = 0; + + } + + //------------------------------------------------------------------------------------------------ + + protected void performValueExpressions(VectorizedRowBatch batch, + int[] allMatchs, int allMatchCount) { + /* + * For the moment, pretend all matched are selected so we can evaluate the value + * expressions. + * + * Since we may use the overflow batch when generating results, we will assign the + * selected and real batch size later... + */ + int[] saveSelected = batch.selected; + batch.selected = allMatchs; + boolean saveSelectedInUse = batch.selectedInUse; + batch.selectedInUse = true; + batch.size = allMatchCount; + + // Run our value expressions over whole batch. + for(VectorExpression ve: bigTableValueExpressions) { + ve.evaluate(batch); + } + + batch.selected = saveSelected; + batch.selectedInUse = saveSelectedInUse; + } + + //------------------------------------------------------------------------------------------------ + + /* + * Common generate join results from hash maps used by Inner and Outer joins. + */ + + /** + * Generate join results for a single small table value match. + * + * @param batch + * The big table batch. + * @param hashMapResult + * The hash map results for the matching key. + * @param allMatchs + * The selection array for all matches key. + * @param allMatchesIndex + * Index into allMatches of the matching key we are generating for. + * @param duplicateCount + * Number of equal key rows. + * @param numSel + * Current number of rows that are remaining in the big table for forwarding. + * @return + * The new count of selected rows. + */ + protected int generateHashMapResultSingleValue(VectorizedRowBatch batch, + VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, + int duplicateCount, int numSel) throws HiveException, IOException { + + // Read single value. + + ByteSegmentRef byteSegmentRef = hashMapResult.first(); + + // Generate result within big table batch itself. + + for (int i = 0; i < duplicateCount; i++) { + + int batchIndex = allMatchs[allMatchesIndex + i]; + + // Outer key copying is only used when we are using the input BigTable batch as the output. + // + if (bigTableVectorCopyOuterKeys != null) { + // Copy within row. + bigTableVectorCopyOuterKeys.copyByReference(batch, batchIndex, batch, batchIndex); + } + + if (smallTableVectorDeserializeRow != null) { + + byte[] bytes = byteSegmentRef.getBytes(); + int offset = (int) byteSegmentRef.getOffset(); + int length = byteSegmentRef.getLength(); + smallTableVectorDeserializeRow.setBytes(bytes, offset, length); + + smallTableVectorDeserializeRow.deserializeByValue(batch, batchIndex); + } + + // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, "generateHashMapResultSingleValue big table"); + + // Use the big table row as output. + batch.selected[numSel++] = batchIndex; + } + + return numSel; + } + + /** + * Generate results for a N x M cross product. + * + * @param batch + * The big table batch. + * @param hashMapResult + * The hash map results for the matching key. + * @param allMatchs + * The all match selected array that contains (physical) batch indices. + * @param allMatchesIndex + * The index of the match key. + * @param duplicateCount + * Number of equal key rows. + */ + protected void generateHashMapResultMultiValue(VectorizedRowBatch batch, + VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, + int duplicateCount) throws HiveException, IOException { + + if (useOverflowRepeatedThreshold && + hashMapResult.isCappedCountAvailable() && + hashMapResult.cappedCount() > overflowRepeatedThreshold) { + + // Large cross product: generate the vector optimization using repeating vectorized + // row batch optimization in the overflow batch. + + generateHashMapResultLargeMultiValue( + batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount); + return; + } + + // We do the cross product of the N big table equal key row's values against the + // small table matching key which has M value rows into overflow batch. + + for (int i = 0; i < duplicateCount; i++) { + + int batchIndex = allMatchs[allMatchesIndex + i]; + + ByteSegmentRef byteSegmentRef = hashMapResult.first(); + while (byteSegmentRef != null) { + + // Copy the BigTable values into the overflow batch. Since the overflow batch may + // not get flushed here, we must copy by value. + // Note this includes any outer join keys that need to go into the small table "area". + if (bigTableRetainedVectorCopy != null) { + bigTableRetainedVectorCopy.copyByValue(batch, batchIndex, + overflowBatch, overflowBatch.size); + } + + if (smallTableVectorDeserializeRow != null) { + + byte[] bytes = byteSegmentRef.getBytes(); + int offset = (int) byteSegmentRef.getOffset(); + int length = byteSegmentRef.getLength(); + smallTableVectorDeserializeRow.setBytes(bytes, offset, length); + + smallTableVectorDeserializeRow.deserializeByValue(overflowBatch, overflowBatch.size); + } + + // VectorizedBatchUtil.debugDisplayOneRow(overflowBatch, overflowBatch.size, "generateHashMapResultMultiValue overflow"); + + overflowBatch.size++; + if (overflowBatch.size == overflowBatch.DEFAULT_SIZE) { + forwardOverflow(); + } + byteSegmentRef = hashMapResult.next(); + } + } + } + + /** + * Generate optimized results for a large N x M cross product using repeated vectorized row + * batch optimization. + * + * @param batch + * The big table batch. + * @param hashMapResult + * The hash map results for the matching key. + * @param allMatchs + * The all match selected array that contains (physical) batch indices. + * @param allMatchesIndex + * The index of the match key. + * @param duplicateCount + * Number of equal key rows. + */ + private void generateHashMapResultLargeMultiValue(VectorizedRowBatch batch, + VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex, + int duplicateCount) throws HiveException, IOException { + + // Kick out previous overflow batch results. + if (overflowBatch.size > 0) { + forwardOverflow(); + } + + ByteSegmentRef byteSegmentRef = hashMapResult.first(); + while (true) { + + // Fill up as much of the overflow batch as possible with small table values. + while (byteSegmentRef != null) { + + if (smallTableVectorDeserializeRow != null) { + + byte[] bytes = byteSegmentRef.getBytes(); + int offset = (int) byteSegmentRef.getOffset(); + int length = byteSegmentRef.getLength(); + smallTableVectorDeserializeRow.setBytes(bytes, offset, length); + + smallTableVectorDeserializeRow.deserializeByValue(overflowBatch, overflowBatch.DEFAULT_SIZE); + } + + overflowBatch.size++; + if (overflowBatch.size == overflowBatch.DEFAULT_SIZE) { + break; + } + byteSegmentRef = hashMapResult.next(); + } + + // Forward the overflow batch over and over: + // + // Reference a new one big table row's values each time + // cross product + // Current "batch" of small table values. + // + // TODO: This could be further optimized to copy big table (equal) keys once + // and only copy big table values each time... + // And, not set repeating every time... + // + + for (int i = 0; i < duplicateCount; i++) { + + int batchIndex = allMatchs[allMatchesIndex + i]; + + if (bigTableRetainedVectorCopy != null) { + // The one big table row's values repeat. + bigTableRetainedVectorCopy.copyByReference(batch, batchIndex, overflowBatch, 0); + for (int column : bigTableRetainedMapping.getOutputColumns()) { + overflowBatch.cols[column].isRepeating = true; + } + } + + // Crucial here that we don't reset the overflow batch, or we will loose the small table + // values we put in above. + forwardOverflowNoReset(); + + // Hand reset the big table columns. + for (int column : bigTableRetainedMapping.getOutputColumns()) { + ColumnVector colVector = overflowBatch.cols[column]; + colVector.reset(); + } + } + + if (hashMapResult.isEof()) { + break; + } + byteSegmentRef = hashMapResult.next(); + + // Get ready for a another round of small table values. + overflowBatch.reset(); + } + // Clear away any residue from our optimizations. + overflowBatch.reset(); + } + + /** + * Generate optimized results when entire batch key is repeated and it matched the hash map. + * + * @param batch + * The big table batch. + * @param hashMapResult + * The hash map results for the repeated key. + */ + protected void generateHashMapResultRepeatedAll(VectorizedRowBatch batch, + VectorMapJoinHashMapResult hashMapResult) throws IOException, HiveException { + + int[] selected = batch.selected; + + if (batch.selectedInUse) { + // The selected array is already filled in as we want it. + } else { + for (int i = 0; i < batch.size; i++) { + selected[i] = i; + } + batch.selectedInUse = true; + } + + int numSel = 0; + if (hashMapResult.isSingleRow()) { + numSel = generateHashMapResultSingleValue(batch, hashMapResult, + batch.selected, 0, batch.size, numSel); + + } else { + generateHashMapResultMultiValue(batch, hashMapResult, + batch.selected, 0, batch.size); + } + + batch.size = numSel; + } + + //----------------------------------------------------------------------------------------------- + + /* + * Spill. + */ + + + private void setupSpillSerDe(VectorizedRowBatch batch) throws HiveException { + + TypeInfo[] inputObjInspectorsTypeInfos = + VectorizedBatchUtil.typeInfosFromStructObjectInspector( + (StructObjectInspector) inputObjInspectors[posBigTable]); + + List projectedColumns = vContext.getProjectedColumns(); + int projectionSize = vContext.getProjectedColumns().size(); + + List typeInfoList = new ArrayList(); + List noNullsProjectionList = new ArrayList(); + for (int i = 0; i < projectionSize; i++) { + int projectedColumn = projectedColumns.get(i); + if (batch.cols[projectedColumn] != null) { + typeInfoList.add(inputObjInspectorsTypeInfos[i]); + noNullsProjectionList.add(projectedColumn); + } + } + + int[] noNullsProjection = ArrayUtils.toPrimitive(noNullsProjectionList.toArray(new Integer[0])); + int noNullsProjectionSize = noNullsProjection.length; + bigTableTypeInfos = typeInfoList.toArray(new TypeInfo[0]); + + bigTableVectorSerializeRow = + new VectorSerializeRow( + new LazyBinarySerializeWrite(noNullsProjectionSize)); + + bigTableVectorSerializeRow.init( + bigTableTypeInfos, + noNullsProjection); + + bigTableVectorDeserializeRow = + new VectorDeserializeRow( + new LazyBinaryDeserializeRead(bigTableTypeInfos)); + + bigTableVectorDeserializeRow.init(noNullsProjection); + } + + private void spillSerializeRow(VectorizedRowBatch batch, int batchIndex, + VectorMapJoinHashTableResult hashTableResult) throws IOException { + + int partitionId = hashTableResult.spillPartitionId(); + + HybridHashTableContainer ht = (HybridHashTableContainer) mapJoinTables[posSingleVectorMapJoinSmallTable]; + HashPartition hp = ht.getHashPartitions()[partitionId]; + + VectorMapJoinRowBytesContainer rowBytesContainer = hp.getMatchfileRowBytesContainer(); + Output output = rowBytesContainer.getOuputForRowBytes(); +// int offset = output.getLength(); + bigTableVectorSerializeRow.setOutputAppend(output); + bigTableVectorSerializeRow.serializeWrite(batch, batchIndex); +// int length = output.getLength() - offset; + rowBytesContainer.finishRow(); + +// LOG.debug("spillSerializeRow spilled batchIndex " + batchIndex + ", length " + length); + } + + protected void spillHashMapBatch(VectorizedRowBatch batch, + VectorMapJoinHashTableResult[] hashTableResults, + int[] spills, int[] spillHashTableResultIndices, int spillCount) + throws HiveException, IOException { + + if (bigTableVectorSerializeRow == null) { + setupSpillSerDe(batch); + } + + for (int i = 0; i < spillCount; i++) { + int batchIndex = spills[i]; + + int hashTableResultIndex = spillHashTableResultIndices[i]; + VectorMapJoinHashTableResult hashTableResult = hashTableResults[hashTableResultIndex]; + + spillSerializeRow(batch, batchIndex, hashTableResult); + } + } + + protected void spillBatchRepeated(VectorizedRowBatch batch, + VectorMapJoinHashTableResult hashTableResult) throws HiveException, IOException { + + if (bigTableVectorSerializeRow == null) { + setupSpillSerDe(batch); + } + + int[] selected = batch.selected; + boolean selectedInUse = batch.selectedInUse; + + for (int logical = 0; logical < batch.size; logical++) { + int batchIndex = (selectedInUse ? selected[logical] : logical); + spillSerializeRow(batch, batchIndex, hashTableResult); + } + } + + @Override + protected void reloadHashTable(byte pos, int partitionId) + throws IOException, HiveException, SerDeException, ClassNotFoundException { + + // The super method will reload a hash table partition of one of the small tables. + // Currently, for native vector map join it will only be one small table. + super.reloadHashTable(pos, partitionId); + + MapJoinTableContainer smallTable = spilledMapJoinTables[pos]; + + vectorMapJoinHashTable = VectorMapJoinOptimizedCreateHashTable.createHashTable(conf, + smallTable); + needHashTableSetup = true; + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " reloadHashTable!"); + } + } + + @Override + protected void reProcessBigTable(int partitionId) + throws HiveException { + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " reProcessBigTable enter..."); + } + + if (spillReplayBatch == null) { + // The process method was not called -- no big table rows. + return; + } + + HashPartition partition = firstSmallTable.getHashPartitions()[partitionId]; + + int rowCount = 0; + int batchCount = 0; + + try { + VectorMapJoinRowBytesContainer bigTable = partition.getMatchfileRowBytesContainer(); + bigTable.prepareForReading(); + + while (bigTable.readNext()) { + rowCount++; + + byte[] bytes = bigTable.currentBytes(); + int offset = bigTable.currentOffset(); + int length = bigTable.currentLength(); + +// LOG.debug(CLASS_NAME + " reProcessBigTable serialized row #" + rowCount + ", offset " + offset + ", length " + length); + + bigTableVectorDeserializeRow.setBytes(bytes, offset, length); + bigTableVectorDeserializeRow.deserializeByValue(spillReplayBatch, spillReplayBatch.size); + spillReplayBatch.size++; + + if (spillReplayBatch.size == VectorizedRowBatch.DEFAULT_SIZE) { + // LOG.debug("reProcessBigTable going to call process with spillReplayBatch.size " + spillReplayBatch.size + " rows"); + process(spillReplayBatch, posBigTable); // call process once we have a full batch + spillReplayBatch.reset(); + batchCount++; + } + } + // Process the row batch that has less than DEFAULT_SIZE rows + if (spillReplayBatch.size > 0) { + // LOG.debug("reProcessBigTable going to call process with spillReplayBatch.size " + spillReplayBatch.size + " rows"); + process(spillReplayBatch, posBigTable); + spillReplayBatch.reset(); + batchCount++; + } + bigTable.clear(); + } catch (Exception e) { + LOG.info(CLASS_NAME + " reProcessBigTable exception! " + e); + throw new HiveException(e); + } + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " reProcessBigTable exit! " + rowCount + " row processed and " + batchCount + " batches processed"); + } + } + + + //----------------------------------------------------------------------------------------------- + + /* + * Forwarding. + */ + + /** + * Forward the big table batch to the children. + * + * @param batch + * The big table batch. + */ + public void forwardBigTableBatch(VectorizedRowBatch batch) throws HiveException { + + // Save original projection. + int[] originalProjections = batch.projectedColumns; + int originalProjectionSize = batch.projectionSize; + + // Project with the output of our operator. + batch.projectionSize = outputProjection.length; + batch.projectedColumns = outputProjection; + + forward(batch, null); + + // Revert the projected columns back, because batch can be re-used by our parent operators. + batch.projectionSize = originalProjectionSize; + batch.projectedColumns = originalProjections; + } + + + /** + * Forward the overflow batch and reset the batch. + */ + protected void forwardOverflow() throws HiveException { + forward(overflowBatch, null); + overflowBatch.reset(); + } + + /** + * Forward the overflow batch, but do not reset the batch. + */ + private void forwardOverflowNoReset() throws HiveException { + forward(overflowBatch, null); + } + + /* + * Close. + */ + + /** + * On close, make sure a partially filled overflow batch gets forwarded. + */ + @Override + public void closeOp(boolean aborted) throws HiveException { + super.closeOp(aborted); + if (!aborted && overflowBatch.size > 0) { + forwardOverflow(); + } + if (isLogDebugEnabled) { + LOG.debug("VectorMapJoinInnerLongOperator closeOp " + batchCounter + " batches processed"); + } + } + + //----------------------------------------------------------------------------------------------- + + /* + * Debug. + */ + + public boolean verifyMonotonicallyIncreasing(int[] selected, int size) { + + if (size == 0) { + return true; + } + int prevBatchIndex = selected[0]; + + for (int i = 1; i < size; i++) { + int batchIndex = selected[i]; + if (batchIndex <= prevBatchIndex) { + return false; + } + prevBatchIndex = batchIndex; + } + return true; + } + + public static String intArrayToRangesString(int selection[], int size) { + if (size == 0) { + return "[]"; + } + + StringBuilder sb = new StringBuilder(); + + // Use ranges and duplicate multipliers to reduce the size of the display. + sb.append("["); + int firstIndex = 0; + int firstValue = selection[0]; + + boolean duplicates = false; + + int i = 1; + for ( ; i < size; i++) { + int newValue = selection[i]; + if (newValue == selection[i - 1]) { + + // Duplicate. + duplicates = true; + + if (newValue == firstValue) { + continue; + } else { + // Prior none, singleton, or range? + int priorRangeLength = i - 1 - firstIndex; + + if (priorRangeLength == 0) { + continue; + } + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 2]); + } + firstIndex = i - 1; + firstValue = newValue; + continue; + } + } else { + if (duplicates) { + int numDuplicates = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(numDuplicates + "*" + firstValue); + duplicates = false; + firstIndex = i; + firstValue = newValue; + continue; + } if (newValue == selection[i - 1] + 1) { + // Continue range.. + continue; + } else { + // Prior singleton or range? + int priorRangeLength = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 1]); + } + firstIndex = i; + firstValue = newValue; + continue; + } + } + } + if (duplicates) { + int numDuplicates = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(numDuplicates + "*" + firstValue); + } else { + // Last singleton or range? + int priorRangeLength = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 1]); + } + } + sb.append("]"); + return sb.toString(); + } + + public static String longArrayToRangesString(long selection[], int size) { + if (size == 0) { + return "[]"; + } + + StringBuilder sb = new StringBuilder(); + + // Use ranges and duplicate multipliers to reduce the size of the display. + sb.append("["); + int firstIndex = 0; + long firstValue = selection[0]; + + boolean duplicates = false; + + int i = 1; + for ( ; i < size; i++) { + long newValue = selection[i]; + if (newValue == selection[i - 1]) { + + // Duplicate. + duplicates = true; + + if (newValue == firstValue) { + continue; + } else { + // Prior none, singleton, or range? + int priorRangeLength = i - 1 - firstIndex; + + if (priorRangeLength == 0) { + continue; + } + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 2]); + } + firstIndex = i - 1; + firstValue = newValue; + continue; + } + } else { + if (duplicates) { + int numDuplicates = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(numDuplicates + "*" + firstValue); + duplicates = false; + firstIndex = i; + firstValue = newValue; + continue; + } if (newValue == selection[i - 1] + 1) { + // Continue range.. + continue; + } else { + // Prior singleton or range? + int priorRangeLength = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 1]); + } + firstIndex = i; + firstValue = newValue; + continue; + } + } + } + if (duplicates) { + int numDuplicates = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(numDuplicates + "*" + firstValue); + } else { + // Last singleton or range? + int priorRangeLength = i - firstIndex; + if (firstIndex > 0) { + sb.append(","); + } + sb.append(firstValue); + if (priorRangeLength > 1) { + sb.append(".." + selection[i - 1]); + } + } + sb.append("]"); + return sb.toString(); + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.rej ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.rej new file mode 100644 index 0000000..d1e90dc --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinGenerateResultOperator.java.rej @@ -0,0 +1,64 @@ +*************** +*** 239,250 **** + overflowBatch, overflowBatch.size); + } + +- // Reference the keys we just copied above. +- if (bigTableVectorCopyOuterKeys != null) { +- bigTableVectorCopyOuterKeys.copyByReference(overflowBatch, overflowBatch.size, +- overflowBatch, overflowBatch.size); +- } +- + if (smallTableVectorDeserializeRow != null) { + + byte[] bytes = byteSegmentRef.getBytes(); +--- 382,387 ---- + overflowBatch, overflowBatch.size); + } + + if (smallTableVectorDeserializeRow != null) { + + byte[] bytes = byteSegmentRef.getBytes(); +*************** +*** 330,341 **** + overflowBatch.cols[column].isRepeating = true; + } + } +- if (bigTableVectorCopyOuterKeys != null) { +- bigTableVectorCopyOuterKeys.copyByReference(batch, batchIndex, overflowBatch, 0); +- for (int column : bigTableOuterKeyOutputVectorColumns) { +- overflowBatch.cols[column].isRepeating = true; +- } +- } + + // Crucial here that we don't reset the overflow batch, or we will loose the small table + // values we put in above. +--- 467,472 ---- + overflowBatch.cols[column].isRepeating = true; + } + } + + // Crucial here that we don't reset the overflow batch, or we will loose the small table + // values we put in above. +*************** +*** 346,358 **** + ColumnVector colVector = overflowBatch.cols[column]; + colVector.reset(); + } +- +- if (bigTableVectorCopyOuterKeys != null) { +- for (int column : bigTableOuterKeyOutputVectorColumns) { +- ColumnVector colVector = overflowBatch.cols[column]; +- colVector.reset(); +- } +- } + } + + if (hashMapResult.isEof()) { +--- 477,482 ---- + ColumnVector colVector = overflowBatch.cols[column]; + colVector.reset(); + } + } + + if (hashMapResult.isEof()) { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyGenerateResultOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyGenerateResultOperator.java index 6b33a39..c075bd2 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyGenerateResultOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyGenerateResultOperator.java @@ -22,13 +22,9 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; @@ -47,42 +43,41 @@ extends VectorMapJoinGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyGenerateResultOperator.class.getName()); - //--------------------------------------------------------------------------- + //------------------------------------------------------------------------------------------------ + + private static final String CLASS_NAME = VectorMapJoinInnerBigOnlyGenerateResultOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ + + //------------------------------------------------------------------------------------------------ // Inner big-table only join specific members. // // An array of hash multi-set results so we can do lookups on the whole batch before output result // generation. - protected transient VectorMapJoinHashMultiSetResult hashMultiSetResults[]; + protected transient MapJoinHashMultiSetResult hashMultiSetResults[]; - // Pre-allocated member for storing the (physical) batch index of matching row (single- or - // multi-small-table-valued) indexes during a process call. - protected transient int[] allMatchs; - - /* - * Pre-allocated members for storing information on single- and multi-valued-small-table matches. - * - * ~ValueCounts - * Number of (empty) small table values. - * ~AllMatchIndices - * (Logical) indices into allMatchs to the first row of a match of a - * possible series of duplicate keys. - * ~DuplicateCounts - * The duplicate count for each matched key. - * - */ - protected transient long[] equalKeySeriesValueCounts; - protected transient int[] equalKeySeriesAllMatchIndices; - protected transient int[] equalKeySeriesDuplicateCounts; + // Pre-allocated member for storing the batch indices of the matched rows. + protected transient int[] matchSelected; + // Pre-allocated member for storing the new logical batch index (within newSelected) and + // series count of rows that matched. + protected transient int[] matchLogicalIndices; + protected transient int[] matchDuplicateCounts; + protected transient long[] matchValueCounts; - // Pre-allocated member for storing the (physical) batch index of rows that need to be spilled. - protected transient int[] spills; + // Pre-allocated member for storing the logical batch index and series count of rows that spilled. + protected transient int[] spillLogicalIndices; + protected transient int[] spillDuplicateCounts; - // Pre-allocated member for storing index into the hashMultiSetResults for each spilled row. - protected transient int[] spillHashMapResultIndices; + // Pre-allocated member for storing the reference to the hash multi-set results of rows that spilled. + protected transient MapJoinHashMultiSetResult[] spillHashMultiSetResults; public VectorMapJoinInnerBigOnlyGenerateResultOperator() { super(); @@ -100,21 +95,20 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { super.commonSetup(batch); // Inner big-table only join specific. - VectorMapJoinHashMultiSet baseHashMultiSet = (VectorMapJoinHashMultiSet) vectorMapJoinHashTable; - - hashMultiSetResults = new VectorMapJoinHashMultiSetResult[batch.DEFAULT_SIZE]; + hashMultiSetResults = new MapJoinHashMultiSetResult[batch.DEFAULT_SIZE]; for (int i = 0; i < hashMultiSetResults.length; i++) { - hashMultiSetResults[i] = baseHashMultiSet.createHashMultiSetResult(); + hashMultiSetResults[i] = vectormapJoinHashTableFactory.createHashMultiSetResult(); } - allMatchs = new int[batch.DEFAULT_SIZE]; + matchSelected = new int[batch.DEFAULT_SIZE]; - equalKeySeriesValueCounts = new long[batch.DEFAULT_SIZE]; - equalKeySeriesAllMatchIndices = new int[batch.DEFAULT_SIZE]; - equalKeySeriesDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchLogicalIndices = new int[batch.DEFAULT_SIZE]; + matchDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchValueCounts = new long[batch.DEFAULT_SIZE]; - spills = new int[batch.DEFAULT_SIZE]; - spillHashMapResultIndices = new int[batch.DEFAULT_SIZE]; + spillLogicalIndices = new int[batch.DEFAULT_SIZE]; + spillDuplicateCounts = new int[batch.DEFAULT_SIZE]; + spillHashMultiSetResults = new MapJoinHashMultiSetResult[batch.DEFAULT_SIZE]; } //----------------------------------------------------------------------------------------------- @@ -129,50 +123,59 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { * @param batch * The big table batch with any matching and any non matching rows both as * selected in use. - * @param allMatchCount - * Number of matches in allMatchs. - * @param equalKeySeriesCount - * Number of single value matches. - * @param spillCount - * Number of spills in spills. - * @param hashTableResults - * The array of all hash table results for the batch. We need the - * VectorMapJoinHashTableResult for the spill information. - * @param hashMapResultCount - * Number of entries in hashMapResults. + * @param matchSeriesCount + * Number of matches in the match* arrays. + * @param matchSelectedCount + * The selected count in matchSelected. + * @param spillSeriesCount + * Number of spills in spill* arrays. * **/ - protected void finishInnerBigOnly(VectorizedRowBatch batch, - int allMatchCount, int equalKeySeriesCount, int spillCount, - VectorMapJoinHashTableResult[] hashTableResults, int hashMapResultCount) - throws HiveException, IOException { + protected void finishInnerBigOnly(VectorizedRowBatch batch, int matchSeriesCount, + int spillSeriesCount) throws HiveException, IOException { + + final int selectedSize = batch.size; + boolean selectedInUse = batch.selectedInUse; + int[] selected = batch.selected; + + // Dump out the spill rows now. + if (spillSeriesCount > 0) { + + spillHashMapBatch(batch, spillLogicalIndices, spillDuplicateCounts, spillHashMultiSetResults, + spillSeriesCount, selectedInUse, selected, selectedSize); - // Get rid of spills before we start modifying the batch. - if (spillCount > 0) { - spillHashMapBatch(batch, hashTableResults, - spills, spillHashMapResultIndices, spillCount); + if (spillSeriesCount == selectedSize) { + batch.size = 0; + batch.selectedInUse = false; + return; + } } + int matchSelectedCount = + flattenLogicalSeriesIntoSelected( + selectedInUse, selected, selectedSize, + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + matchSelected); + /* * Optimize by running value expressions only over the matched rows. */ - if (allMatchCount > 0 && bigTableValueExpressions != null) { - performValueExpressions(batch, allMatchs, allMatchCount); + if (matchSeriesCount > 0 && bigTableValueExpressions != null) { + performValueExpressions(batch, matchSelected, matchSelectedCount); } int numSel = 0; - for (int i = 0; i < equalKeySeriesCount; i++) { - long count = equalKeySeriesValueCounts[i]; - int allMatchesIndex = equalKeySeriesAllMatchIndices[i]; - int duplicateCount = equalKeySeriesDuplicateCounts[i]; + for (int i = 0; i < matchSeriesCount; i++) { + int logical = matchLogicalIndices[i]; + int duplicateCount = matchDuplicateCounts[i]; + long count = matchValueCounts[i]; if (count == 1) { - numSel = generateHashMultiSetResultSingleValue( - batch, allMatchs, allMatchesIndex, duplicateCount, numSel); + numSel = generateHashMultiSetResultSingleValue(batch, + matchSelected, logical, duplicateCount, numSel); } else { generateHashMultiSetResultMultiValue(batch, - allMatchs, allMatchesIndex, - duplicateCount, count); + matchSelected, logical, duplicateCount, count); } } batch.size = numSel; @@ -259,70 +262,4 @@ private void generateHashMultiSetResultMultiValue(VectorizedRowBatch batch, } } } - - /** - * Generate the inner big table only join output results for one vectorized row batch with - * a repeated key. - * - * @param batch - * The big table batch with any matching and any non matching rows both as - * selected in use. - * @param hashMultiSetResult - * The hash multi-set results for the batch. - */ - protected int generateHashMultiSetResultRepeatedAll(VectorizedRowBatch batch, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws HiveException { - - long count = hashMultiSetResult.count(); - - if (batch.selectedInUse) { - // The selected array is already filled in as we want it. - } else { - int[] selected = batch.selected; - for (int i = 0; i < batch.size; i++) { - selected[i] = i; - } - batch.selectedInUse = true; - } - - do { - forwardBigTableBatch(batch); - count--; - } while (count > 0); - - // We forwarded the batch in this method. - return 0; - } - - protected void finishInnerBigOnlyRepeated(VectorizedRowBatch batch, JoinUtil.JoinResult joinResult, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws HiveException, IOException { - - switch (joinResult) { - case MATCH: - - if (bigTableValueExpressions != null) { - // Run our value expressions over whole batch. - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } - } - - // Generate special repeated case. - int numSel = generateHashMultiSetResultRepeatedAll(batch, hashMultiSetResult); - batch.size = numSel; - batch.selectedInUse = true; - break; - - case SPILL: - // Whole batch is spilled. - spillBatchRepeated(batch, (VectorMapJoinHashTableResult) hashMultiSetResult); - batch.size = 0; - break; - - case NOMATCH: - // No match for entire batch. - batch.size = 0; - break; - } - } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyLongOperator.java index 9e77d22..289f767 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyLongOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyLongOperator.java @@ -23,19 +23,16 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; - -// Single-Column Long hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMultiSet; - -// Single-Column Long specific imports. -import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; /* * Specialized class for doing a vectorized map join that is an inner join on a Single-Column Long @@ -44,8 +41,17 @@ public class VectorMapJoinInnerBigOnlyLongOperator extends VectorMapJoinInnerBigOnlyGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerBigOnlyLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -54,7 +60,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinLongHashMultiSet hashMultiSet; + private transient MapJoinHashTableFind hashMultiSet; //--------------------------------------------------------------------------- // Single-Column Long specific members. @@ -67,6 +73,10 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + private transient PrimitiveTypeInfo singleJoinColumnPrimitiveTypeInfo; + + // The object that determines equal key series. + private transient VectorKeySeriesLong longKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -101,6 +111,10 @@ public void process(Object row, int tag) throws HiveException { */ singleJoinColumn = bigTableKeyColumnMap[0]; + singleJoinColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) bigTableKeyTypeInfos[0]; + + longKeySeries = + new VectorKeySeriesLong(singleJoinColumn, singleJoinColumnPrimitiveTypeInfo); needCommonSetup = false; } @@ -112,8 +126,7 @@ public void process(Object row, int tag) throws HiveException { /* * Get our Single-Column Long hash multi-set information for this specialized class. */ - - hashMultiSet = (VectorMapJoinLongHashMultiSet) vectorMapJoinHashTable; + hashMultiSet = vectorMapJoinHashTableFind; useMinMax = hashMultiSet.useMinMax(); if (useMinMax) { min = hashMultiSet.min(); @@ -124,6 +137,7 @@ public void process(Object row, int tag) throws HiveException { } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner big-only join. @@ -135,9 +149,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -151,232 +165,89 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Single-Column Long specific declarations. - */ + longKeySeries.processBatch(batch); - // The one join column for this specialized class. - LongColumnVector joinColVector = (LongColumnVector) batch.cols[singleJoinColumn]; - long[] vector = joinColVector.vector; + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMultiSetResultCount = 0; - /* - * Single-Column Long check for repeating. - */ + MapJoinHashMultiSetResult hashMultiSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + long key; + do { + // Use the next hash multi-set result entry. + hashMultiSetResult = hashMultiSetResults[hashMultiSetResultCount]; - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + if (longKeySeries.getCurrentIsAllNull()) { - if (allKeyInputColumnsRepeating) { + // CONSIDER: Add support for NullSafe option. - /* - * Repeating. - */ + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. - - /* - * Single-Column Long specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - long key = vector[0]; + + key = longKeySeries.getCurrentKey(); if (useMinMax && (key < min || key > max)) { // Out of range for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; } else { - joinResult = hashMultiSet.contains(key, hashMultiSetResults[0]); + hashMultiSet.hashMultiSetContains( + key, + longKeySeries.getCurrentHashCode(), + hashMultiSetResult); + containsResult = hashMultiSetResult.getMapJoinResult(); } - } - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); } - finishInnerBigOnlyRepeated(batch, joinResult, hashMultiSetResults[0]); - } else { /* - * NOT Repeating. + * Common inner join result processing. */ - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); + switch (containsResult) { + case MATCH: + // We have extracted the existence and count from the hash multi-set result, so we don't + // keep it. + matchLogicalIndices[matchSeriesCount] = longKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + matchValueCounts[matchSeriesCount] = hashMultiSetResult.count(); + matchSeriesCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = longKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + spillHashMultiSetResults[spillSeriesCount] = hashMultiSetResult; + spillSeriesCount++; + hashMultiSetResultCount++; + spilledRowCounter += longKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMultiSetResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - - /* - * Single-Column Long specific variables. - */ - - long saveKey = 0; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column Long get key. - */ - - long currentKey; - boolean isNull; - if (!joinColVector.noNulls && joinColVector.isNull[batchIndex]) { - currentKey = 0; - isNull = true; - } else { - currentKey = vector[batchIndex]; - isNull = false; - } - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || currentKey != saveKey) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column Long specific save key. - */ - - saveKey = currentKey; - - /* - * Single-Column Long specific lookup key. - */ - - if (useMinMax && (currentKey < min || currentKey > max)) { - // Key out of range for whole hash table. - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - } else { - saveJoinResult = hashMultiSet.contains(currentKey, hashMultiSetResults[hashMultiSetResultCount]); - } - } - - /* - * Common inner big-only join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesValueCounts[equalKeySeriesCount] = hashMultiSetResults[hashMultiSetResultCount].count(); - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } + if (!longKeySeries.next()) { + break; } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " equalKeySeriesValueCounts " + longArrayToRangesString(equalKeySeriesValueCounts, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesDuplicateCounts " + intArrayToRangesString(equalKeySeriesDuplicateCounts, equalKeySeriesCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMultiSetResults, 0, hashMultiSetResultCount))); - } - - finishInnerBigOnly(batch, - allMatchCount, equalKeySeriesCount, spillCount, - (VectorMapJoinHashTableResult[]) hashMultiSetResults, hashMultiSetResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishInnerBigOnly(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyMultiKeyOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyMultiKeyOperator.java index e4f6c5d..b51584c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyMultiKeyOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyMultiKeyOperator.java @@ -23,20 +23,17 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesMultiFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; -// Multi-Key hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet; - // Multi-Key specific imports. -import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; -import org.apache.hadoop.hive.serde2.ByteStream.Output; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; /* @@ -47,8 +44,17 @@ public class VectorMapJoinInnerBigOnlyMultiKeyOperator extends VectorMapJoinInnerBigOnlyGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyMultiKeyOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerBigOnlyMultiKeyOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -57,21 +63,17 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMultiSet hashMultiSet; + private transient MapJoinHashTableFind hashMultiSet; //--------------------------------------------------------------------------- // Multi-Key specific members. // - // Object that can take a set of columns in row in a vectorized row batch and serialized it. - // Known to not have any nulls. - private transient VectorSerializeRow keyVectorSerializeWrite; + // Binary sortable multi-key serializer. + private transient BinarySortableSerializeWrite binarySortableSerializeWrite; - // The BinarySortable serialization of the current key. - private transient Output currentKeyOutput; - - // The BinarySortable serialization of the saved key for a possible series of equal keys. - private transient Output saveKeyOutput; + // The object that determines equal key series. + private transient VectorKeySeriesMultiFast serializedMultiKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -105,12 +107,15 @@ public void process(Object row, int tag) throws HiveException { * Initialize Multi-Key members for this specialized class. */ - keyVectorSerializeWrite = new VectorSerializeRow( - new BinarySortableSerializeWrite(bigTableKeyColumnMap.length)); - keyVectorSerializeWrite.init(bigTableKeyTypeNames, bigTableKeyColumnMap); + binarySortableSerializeWrite = + new BinarySortableSerializeWrite(bigTableKeyColumnMap.length); - currentKeyOutput = new Output(); - saveKeyOutput = new Output(); + // For Multi-Key the Fast hash table computes the serialized key's hash code, + // so we don't use VectorKeySeriesMulti's hash code. + serializedMultiKeySeries = + new VectorKeySeriesMultiFast( + binarySortableSerializeWrite); + serializedMultiKeySeries.init(bigTableKeyTypeInfos, bigTableKeyColumnMap); needCommonSetup = false; } @@ -123,12 +128,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Multi-Key hash multi-set information for this specialized class. */ - hashMultiSet = (VectorMapJoinBytesHashMultiSet) vectorMapJoinHashTable; + hashMultiSet = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner big-only join. @@ -140,9 +146,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -156,234 +162,85 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Multi-Key specific declarations. - */ - - // None. - - /* - * Multi-Key check for repeating. - */ - - // If all BigTable input columns to key expressions are isRepeating, then - // calculate key once; lookup once. - boolean allKeyInputColumnsRepeating; - if (bigTableKeyColumnMap.length == 0) { - allKeyInputColumnsRepeating = false; - } else { - allKeyInputColumnsRepeating = true; - for (int i = 0; i < bigTableKeyColumnMap.length; i++) { - if (!batch.cols[bigTableKeyColumnMap[i]].isRepeating) { - allKeyInputColumnsRepeating = false; - break; - } - } - } + serializedMultiKeySeries.processBatch(batch); - if (allKeyInputColumnsRepeating) { + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMultiSetResultCount = 0; - /* - * Repeating. - */ + MapJoinHashMultiSetResult hashMultiSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + do { + // Use the next hash multi-set result entry. + hashMultiSetResult = hashMultiSetResults[hashMultiSetResultCount]; - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + // NOTE: Any null column in the key for inner join is a non-match. + if (serializedMultiKeySeries.getCurrentHasAnyNulls()) { - /* - * Multi-Key specific repeated lookup. - */ + // CONSIDER: Add support for NullSafe option. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, 0); - JoinUtil.JoinResult joinResult; - if (keyVectorSerializeWrite.getHasAnyNulls()) { - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - byte[] keyBytes = currentKeyOutput.getData(); - int keyLength = currentKeyOutput.getLength(); - joinResult = hashMultiSet.contains(keyBytes, 0, keyLength, hashMultiSetResults[0]); - } + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishInnerBigOnlyRepeated(batch, joinResult, hashMultiSetResults[0]); - } else { + } else { - /* - * NOT Repeating. - */ + hashMultiSet.hashMultiSetContains( + serializedMultiKeySeries.getSerializedBytes(), + serializedMultiKeySeries.getSerializedStart(), + serializedMultiKeySeries.getSerializedLength(), + serializedMultiKeySeries.getCurrentHashCode(), + hashMultiSetResult); + containsResult = hashMultiSetResult.getMapJoinResult(); - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMultiSetResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - /* - * Multi-Key specific variables. + * Common inner join result processing. */ - Output temp; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Multi-Key get key. - */ - - // Generate binary sortable key for current row in vectorized row batch. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, batchIndex); - boolean isAnyNulls = keyVectorSerializeWrite.getHasAnyNulls(); - - /* - * Equal key series checking. - */ - - if (isAnyNulls || !haveSaveKey || !saveKeyOutput.arraysEquals(currentKeyOutput)) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isAnyNulls) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Multi-Key specific save key. - */ - - temp = saveKeyOutput; - saveKeyOutput = currentKeyOutput; - currentKeyOutput = temp; - - /* - * Single-Column Long specific lookup key. - */ - - byte[] keyBytes = saveKeyOutput.getData(); - int keyLength = saveKeyOutput.getLength(); - saveJoinResult = hashMultiSet.contains(keyBytes, 0, keyLength, hashMultiSetResults[hashMultiSetResultCount]); - } - - /* - * Common inner big-only join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesValueCounts[equalKeySeriesCount] = hashMultiSetResults[hashMultiSetResultCount].count(); - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } + switch (containsResult) { + case MATCH: + // We have extracted the existence and count from the hash multi-set result, so we don't + // keep it. + matchLogicalIndices[matchSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + matchValueCounts[matchSeriesCount] = hashMultiSetResult.count(); + matchSeriesCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + spillHashMultiSetResults[spillSeriesCount] = hashMultiSetResult; + spillSeriesCount++; + hashMultiSetResultCount++; + spilledRowCounter += serializedMultiKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } + if (!serializedMultiKeySeries.next()) { + break; } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " equalKeySeriesValueCounts " + longArrayToRangesString(equalKeySeriesValueCounts, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesDuplicateCounts " + intArrayToRangesString(equalKeySeriesDuplicateCounts, equalKeySeriesCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMultiSetResults, 0, hashMultiSetResultCount))); - } - - finishInnerBigOnly(batch, - allMatchCount, equalKeySeriesCount, spillCount, - (VectorMapJoinHashTableResult[]) hashMultiSetResults, hashMultiSetResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishInnerBigOnly(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyStringOperator.java index 2711b10..480d89c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyStringOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerBigOnlyStringOperator.java @@ -23,20 +23,17 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytes; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column String hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet; - -// Single-Column String specific imports. -import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; /* * Specialized class for doing a vectorized map join that is an inner join on a Single-Column String @@ -45,8 +42,17 @@ public class VectorMapJoinInnerBigOnlyStringOperator extends VectorMapJoinInnerBigOnlyGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyStringOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerBigOnlyStringOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -55,7 +61,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMultiSet hashMultiSet; + private transient MapJoinHashTableFind hashMultiSet; //--------------------------------------------------------------------------- // Single-Column String specific members. @@ -64,6 +70,9 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + // The object that determines equal key series. + private transient VectorKeySeriesBytes bytesKeySeries; + //--------------------------------------------------------------------------- // Pass-thru constructors. // @@ -98,6 +107,8 @@ public void process(Object row, int tag) throws HiveException { singleJoinColumn = bigTableKeyColumnMap[0]; + bytesKeySeries = new VectorKeySeriesBytes(singleJoinColumn); + needCommonSetup = false; } @@ -109,12 +120,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column String hash multi-set information for this specialized class. */ - hashMultiSet = (VectorMapJoinBytesHashMultiSet) vectorMapJoinHashTable; + hashMultiSet = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner big-only join. @@ -126,9 +138,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -142,227 +154,84 @@ public void process(Object row, int tag) throws HiveException { } } - // We rebuild in-place the selected array with rows destine to be forwarded. - int numSel = 0; + bytesKeySeries.processBatch(batch); - /* - * Single-Column String specific declarations. - */ + int matchCount = 0; + int spillCount = 0; + int hashMultiSetResultCount = 0; - // The one join column for this specialized class. - BytesColumnVector joinColVector = (BytesColumnVector) batch.cols[singleJoinColumn]; - byte[][] vector = joinColVector.vector; - int[] start = joinColVector.start; - int[] length = joinColVector.length; + MapJoinHashMultiSetResult hashMultiSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + do { + // Use the next hash multi-set result entry. + hashMultiSetResult = hashMultiSetResults[hashMultiSetResultCount]; - /* - * Single-Column String check for repeating. - */ + if (bytesKeySeries.getCurrentIsAllNull()) { - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + // CONSIDER: Add support for NullSafe option. - if (allKeyInputColumnsRepeating) { + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Repeating. - */ - - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. - - /* - * Single-Column String specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - byte[] keyBytes = vector[0]; - int keyStart = start[0]; - int keyLength = length[0]; - joinResult = hashMultiSet.contains(keyBytes, keyStart, keyLength, hashMultiSetResults[0]); - } - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishInnerBigOnlyRepeated(batch, joinResult, hashMultiSetResults[0]); - } else { - - /* - * NOT Repeating. - */ + hashMultiSet.hashMultiSetContains( + bytesKeySeries.getCurrentBytes(), + bytesKeySeries.getCurrentStart(), + bytesKeySeries.getCurrentLength(), + bytesKeySeries.getCurrentHashCode(), + hashMultiSetResult); + containsResult = hashMultiSetResult.getMapJoinResult(); - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMultiSetResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - /* - * Single-Column String specific variables. + * Common inner join result processing. */ - int saveKeyBatchIndex = -1; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column String get key. - */ - - // Implicit -- use batchIndex. - boolean isNull = !joinColVector.noNulls && joinColVector.isNull[batchIndex]; - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || - StringExpr.equal(vector[saveKeyBatchIndex], start[saveKeyBatchIndex], length[saveKeyBatchIndex], - vector[batchIndex], start[batchIndex], length[batchIndex]) == false) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column String specific save key. - */ - - saveKeyBatchIndex = batchIndex; - - /* - * Single-Column String specific lookup key. - */ - - byte[] keyBytes = vector[batchIndex]; - int keyStart = start[batchIndex]; - int keyLength = length[batchIndex]; - saveJoinResult = hashMultiSet.contains(keyBytes, keyStart, keyLength, hashMultiSetResults[hashMultiSetResultCount]); - } - - /* - * Common inner big-only join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesValueCounts[equalKeySeriesCount] = hashMultiSetResults[hashMultiSetResultCount].count(); - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMultiSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the count from the hash multi-set result, so we don't keep it. - equalKeySeriesCount++; - break; - case SPILL: - // We keep the hash multi-set result for its spill information. - hashMultiSetResultCount++; - break; - case NOMATCH: - break; - } + switch (containsResult) { + case MATCH: + // We have extracted the existence and count from the hash multi-set result, so we don't + // keep it. + matchLogicalIndices[matchCount] = bytesKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchCount] = bytesKeySeries.getCurrentDuplicateCount(); + matchValueCounts[matchCount] = hashMultiSetResult.count(); + matchCount++; + break; + + case SPILL: + spillLogicalIndices[spillCount] = bytesKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillCount] = bytesKeySeries.getCurrentDuplicateCount(); + spillHashMultiSetResults[spillCount] = hashMultiSetResult; + spillCount++; + hashMultiSetResultCount++; + spilledRowCounter += bytesKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " equalKeySeriesValueCounts " + longArrayToRangesString(equalKeySeriesValueCounts, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesDuplicateCounts " + intArrayToRangesString(equalKeySeriesDuplicateCounts, equalKeySeriesCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMultiSetResults, 0, hashMultiSetResultCount))); + if (!bytesKeySeries.next()) { + break; } - - finishInnerBigOnly(batch, - allMatchCount, equalKeySeriesCount, spillCount, - (VectorMapJoinHashTableResult[]) hashMultiSetResults, hashMultiSetResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchCount " + matchCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchCount)) + + " spillCount " + spillCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillCount))); } + finishInnerBigOnly(batch, matchCount, spillCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerGenerateResultOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerGenerateResultOperator.java index 36d0611..e362599 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerGenerateResultOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerGenerateResultOperator.java @@ -19,17 +19,14 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin; import java.io.IOException; +import java.util.Arrays; import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; @@ -58,36 +55,25 @@ // An array of hash map results so we can do lookups on the whole batch before output result // generation. - protected transient VectorMapJoinHashMapResult hashMapResults[]; + protected transient MapJoinHashMapResult[] hashMapResults; - // Pre-allocated member for storing the (physical) batch index of matching row (single- or - // multi-small-table-valued) indexes during a process call. - protected transient int[] allMatchs; + // Pre-allocated member for storing the new logical batch index (within matchSelected), + // series count of rows, and hash map results that matched. + protected transient int[] matchLogicalIndices; + protected transient int[] matchDuplicateCounts; + protected transient boolean[] matchIsSingleValue; + protected transient MapJoinHashMapResult[] matchHashMapResults; - /* - * Pre-allocated members for storing information equal key series for small-table matches. - * - * ~HashMapResultIndices - * Index into the hashMapResults array for the match. - * ~AllMatchIndices - * (Logical) indices into allMatchs to the first row of a match of a - * possible series of duplicate keys. - * ~IsSingleValue - * Whether there is 1 or multiple small table values. - * ~DuplicateCounts - * The duplicate count for each matched key. - * - */ - protected transient int[] equalKeySeriesHashMapResultIndices; - protected transient int[] equalKeySeriesAllMatchIndices; - protected transient boolean[] equalKeySeriesIsSingleValue; - protected transient int[] equalKeySeriesDuplicateCounts; + // Pre-allocated member for storing the logical batch index and series count of rows that spilled. + protected transient int[] spillLogicalIndices; + protected transient int[] spillDuplicateCounts; - // Pre-allocated member for storing the (physical) batch index of rows that need to be spilled. - protected transient int[] spills; + // Pre-allocated member for storing the reference to the hash map results of rows that spilled. + protected transient MapJoinHashMapResult[] spillHashMapResults; - // Pre-allocated member for storing index into the hashMapResults for each spilled row. - protected transient int[] spillHashMapResultIndices; + // Pre-allocated member for storing the batch indices of the matched rows. + protected transient int[] matchSelected; + protected transient int[] resultSelected; public VectorMapJoinInnerGenerateResultOperator() { super(); @@ -105,22 +91,23 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { super.commonSetup(batch); // Inner join specific. - VectorMapJoinHashMap baseHashMap = (VectorMapJoinHashMap) vectorMapJoinHashTable; - - hashMapResults = new VectorMapJoinHashMapResult[batch.DEFAULT_SIZE]; + hashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; for (int i = 0; i < hashMapResults.length; i++) { - hashMapResults[i] = baseHashMap.createHashMapResult(); + hashMapResults[i] = vectormapJoinHashTableFactory.createHashMapResult(); } - allMatchs = new int[batch.DEFAULT_SIZE]; + matchLogicalIndices = new int[batch.DEFAULT_SIZE]; + matchDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchIsSingleValue = new boolean[batch.DEFAULT_SIZE]; + matchHashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; + + spillLogicalIndices = new int[batch.DEFAULT_SIZE]; + spillDuplicateCounts = new int[batch.DEFAULT_SIZE]; + spillHashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; - equalKeySeriesHashMapResultIndices = new int[batch.DEFAULT_SIZE]; - equalKeySeriesAllMatchIndices = new int[batch.DEFAULT_SIZE]; - equalKeySeriesIsSingleValue = new boolean[batch.DEFAULT_SIZE]; - equalKeySeriesDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchSelected = new int[batch.DEFAULT_SIZE]; + resultSelected = new int[batch.DEFAULT_SIZE]; - spills = new int[batch.DEFAULT_SIZE]; - spillHashMapResultIndices = new int[batch.DEFAULT_SIZE]; } /* @@ -147,82 +134,80 @@ protected void innerPerBatchSetup(VectorizedRowBatch batch) { * @param batch * The big table batch with any matching and any non matching rows both as * selected in use. - * @param allMatchCount - * Number of matches in allMatchs. - * @param equalKeySeriesCount - * Number of single value matches. - * @param spillCount - * Number of spills in spills. - * @param hashMapResultCount - * Number of entries in hashMapResults. + * @param matchSeriesCount + * Number of matches in the match* arrays. + * @param matchSelectedCount + * The selected count in matchSelected. + * @param spillSeriesCount + * Number of spills in spill* arrays. */ - protected void finishInner(VectorizedRowBatch batch, - int allMatchCount, int equalKeySeriesCount, int spillCount, int hashMapResultCount) - throws HiveException, IOException { + protected void finishInner(VectorizedRowBatch batch, int matchSeriesCount, int spillSeriesCount) + throws HiveException, IOException { - int numSel = 0; + final int size = batch.size; + boolean selectedInUse = batch.selectedInUse; + int[] selected = batch.selected; - /* - * Optimize by running value expressions only over the matched rows. - */ - if (allMatchCount > 0 && bigTableValueExpressions != null) { - performValueExpressions(batch, allMatchs, allMatchCount); - } + // Dump out the spill rows now. + if (spillSeriesCount > 0) { - for (int i = 0; i < equalKeySeriesCount; i++) { - int hashMapResultIndex = equalKeySeriesHashMapResultIndices[i]; - VectorMapJoinHashMapResult hashMapResult = hashMapResults[hashMapResultIndex]; - int allMatchesIndex = equalKeySeriesAllMatchIndices[i]; - boolean isSingleValue = equalKeySeriesIsSingleValue[i]; - int duplicateCount = equalKeySeriesDuplicateCounts[i]; + spillHashMapBatch(batch, spillLogicalIndices, spillDuplicateCounts, spillHashMapResults, + spillSeriesCount, selectedInUse, selected, size); - if (isSingleValue) { - numSel = generateHashMapResultSingleValue( - batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount, numSel); - } else { - generateHashMapResultMultiValue( - batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount); + if (spillSeriesCount == size) { + batch.size = 0; + batch.selectedInUse = false; + return; } } - if (spillCount > 0) { - spillHashMapBatch(batch, (VectorMapJoinHashTableResult[]) hashMapResults, - spills, spillHashMapResultIndices, spillCount); - } + /* + * Optimize by running value expressions only over the matched rows. + */ + int matchSelectedCount = 0; + if (matchSeriesCount > 0 && bigTableValueExpressions != null) { - batch.size = numSel; - batch.selectedInUse = true; - } + // Create matchSelected by adding the batch indices for the match logical index ranges from + // the input batch range. + matchSelectedCount = + flattenLogicalSeriesIntoSelected( + selectedInUse, selected, size, + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + matchSelected); - protected void finishInnerRepeated(VectorizedRowBatch batch, JoinUtil.JoinResult joinResult, - VectorMapJoinHashTableResult hashMapResult) throws HiveException, IOException { + performValueExpressions(batch, matchSelected, matchSelectedCount); - int numSel = 0; + } - switch (joinResult) { - case MATCH: + // Output matches + for (int i = 0; i < matchSeriesCount; i++) { + MapJoinHashMapResult hashMapResult = matchHashMapResults[i]; + int logical = matchLogicalIndices[i]; + int duplicateCount = matchDuplicateCounts[i]; - if (bigTableValueExpressions != null) { - // Run our value expressions over whole batch. - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } + if (matchIsSingleValue[i]) { + generateHashMapResultSingleValue(batch, hashMapResult, + logical, duplicateCount, selectedInUse, selected, size); + } else { + generateHashMapResultMultiValue(batch, hashMapResult, + logical, duplicateCount, selectedInUse, selected, size); } + } - // Generate special repeated case. - generateHashMapResultRepeatedAll(batch, hashMapResults[0]); - break; + // Create selected by not including match logical indices for multi-value small table results + // from input batch logical range, and then putting the batch indices in selected. - case SPILL: - // Whole batch is spilled. - spillBatchRepeated(batch, (VectorMapJoinHashTableResult) hashMapResults[0]); - batch.size = 0; - break; + int numSel = + makeMatchSelectedWithoutMultiValues( + selectedInUse, selected, size, + matchLogicalIndices, matchDuplicateCounts, matchIsSingleValue, matchSeriesCount, + resultSelected); - case NOMATCH: - // No match for entire batch. - batch.size = 0; - break; + batch.selectedInUse = true; + batch.size = numSel; + if (numSel > 0) { + System.arraycopy(resultSelected, 0, selected, 0, numSel); } + // LOG.info("VectorMapJoinInnerLongOperator result selected " + Arrays.toString(Arrays.copyOfRange(selected, 0, batch.size))); } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerLongOperator.java index 0197225..ad79564 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerLongOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerLongOperator.java @@ -23,18 +23,19 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; -// Single-Column Long hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMap; - // Single-Column Long specific imports. -import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; /* * Specialized class for doing a vectorized map join that is an inner join on a Single-Column Long @@ -43,8 +44,17 @@ public class VectorMapJoinInnerLongOperator extends VectorMapJoinInnerGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -53,7 +63,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinLongHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Single-Column Long specific members. @@ -66,6 +76,10 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + private transient PrimitiveTypeInfo singleJoinColumnPrimitiveTypeInfo; + + // The object that determines equal key series. + private transient VectorKeySeriesLong longKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -100,6 +114,10 @@ public void process(Object row, int tag) throws HiveException { */ singleJoinColumn = bigTableKeyColumnMap[0]; + singleJoinColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) bigTableKeyTypeInfos[0]; + + longKeySeries = + new VectorKeySeriesLong(singleJoinColumn, singleJoinColumnPrimitiveTypeInfo); needCommonSetup = false; } @@ -112,7 +130,7 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column Long hash map information for this specialized class. */ - hashMap = (VectorMapJoinLongHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; useMinMax = hashMap.useMinMax(); if (useMinMax) { min = hashMap.min(); @@ -123,25 +141,45 @@ public void process(Object row, int tag) throws HiveException { } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner join. innerPerBatchSetup(batch); + /* + LOG.info(CLASS_NAME + " input batch column count " + batch.numCols); + for (int column = 0; column < batch.numCols; column++) { + LOG.info(CLASS_NAME + " input batch column " + column + " type " + (batch.cols[column] == null ? "NULL" : batch.cols[column].getClass().getSimpleName())); + } + LOG.info(CLASS_NAME + " size " + batch.size); + if (batch.selectedInUse) { + LOG.info(CLASS_NAME + " selected " + Arrays.toString(Arrays.copyOfRange(batch.selected, 0, batch.size))); + } + VectorizedBatchUtil.debugDisplayBatch(batch, CLASS_NAME); + */ + // For inner joins, we may apply the filter(s) now. for(VectorExpression ve : bigTableFilterExpressions) { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } return; } + /* + LOG.info(CLASS_NAME + " filtered size " + batch.size); + if (batch.selectedInUse) { + LOG.info(CLASS_NAME + " filtered selected " + Arrays.toString(Arrays.copyOfRange(batch.selected, 0, batch.size))); + } + */ + // Perform any key expressions. Results will go into scratch columns. if (bigTableKeyExpressions != null) { for (VectorExpression ve : bigTableKeyExpressions) { @@ -149,231 +187,89 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Single-Column Long specific declarations. - */ + longKeySeries.processBatch(batch); - // The one join column for this specialized class. - LongColumnVector joinColVector = (LongColumnVector) batch.cols[singleJoinColumn]; - long[] vector = joinColVector.vector; + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMapResultCount = 0; - /* - * Single-Column Long check for repeating. - */ + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + long key; + do { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + if (longKeySeries.getCurrentIsAllNull()) { - if (allKeyInputColumnsRepeating) { - - /* - * Repeating. - */ + // CONSIDER: Add support for NullSafe option. - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Single-Column Long specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - long key = vector[0]; + + key = longKeySeries.getCurrentKey(); if (useMinMax && (key < min || key > max)) { // Out of range for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; } else { - joinResult = hashMap.lookup(key, hashMapResults[0]); + hashMap.hashMapLookup( + key, + longKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); } - } - - /* - * Common repeated join result processing. - */ - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); } - finishInnerRepeated(batch, joinResult, hashMapResults[0]); - } else { /* - * NOT Repeating. + * Common inner join result processing. */ - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); - } - - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - - /* - * Single-Column Long specific variables. - */ - - long saveKey = 0; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column Long get key. - */ - - long currentKey; - boolean isNull; - if (!joinColVector.noNulls && joinColVector.isNull[batchIndex]) { - currentKey = 0; - isNull = true; - } else { - currentKey = vector[batchIndex]; - isNull = false; - } - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || currentKey != saveKey) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column Long specific save key. - */ - - saveKey = currentKey; - - /* - * Single-Column Long specific lookup key. - */ - - if (useMinMax && (currentKey < min || currentKey > max)) { - // Key out of range for whole hash table. - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - } else { - saveJoinResult = hashMap.lookup(currentKey, hashMapResults[hashMapResultCount]); - } - } - - /* - * Common inner join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } + switch (lookupResult) { + case MATCH: + matchLogicalIndices[matchSeriesCount] = longKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; + hashMapResultCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = longKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; + hashMapResultCount++; + spilledRowCounter += longKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); + if (!longKeySeries.next()) { + break; } - - finishInner(batch, - allMatchCount, equalKeySeriesCount, spillCount, hashMapResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishInner(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerMultiKeyOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerMultiKeyOperator.java index 837d97b..1c6f32a 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerMultiKeyOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerMultiKeyOperator.java @@ -23,19 +23,18 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesMultiFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; -// Multi-Key hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; // Multi-Key specific imports. -import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; -import org.apache.hadoop.hive.serde2.ByteStream.Output; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; /* @@ -45,8 +44,17 @@ public class VectorMapJoinInnerMultiKeyOperator extends VectorMapJoinInnerGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerMultiKeyOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerMultiKeyOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerMultiKeyOperator.class.getName()); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -55,21 +63,17 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Multi-Key specific members. // - // Object that can take a set of columns in row in a vectorized row batch and serialized it. - // Known to not have any nulls. - private transient VectorSerializeRow keyVectorSerializeWrite; - - // The BinarySortable serialization of the current key. - private transient Output currentKeyOutput; + // Binary sortable multi-key serializer. + private transient BinarySortableSerializeWrite binarySortableSerializeWrite; - // The BinarySortable serialization of the saved key for a possible series of equal keys. - private transient Output saveKeyOutput; + // The object that determines equal key series. + private transient VectorKeySeriesMultiFast serializedMultiKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -103,12 +107,15 @@ public void process(Object row, int tag) throws HiveException { * Initialize Multi-Key members for this specialized class. */ - keyVectorSerializeWrite = new VectorSerializeRow( - new BinarySortableSerializeWrite(bigTableKeyColumnMap.length)); - keyVectorSerializeWrite.init(bigTableKeyTypeNames, bigTableKeyColumnMap); + binarySortableSerializeWrite = + new BinarySortableSerializeWrite(bigTableKeyColumnMap.length); - currentKeyOutput = new Output(); - saveKeyOutput = new Output(); + // For Multi-Key the Fast hash table computes the serialized key's hash code, + // so we don't use VectorKeySeriesMulti's hash code. + serializedMultiKeySeries = + new VectorKeySeriesMultiFast( + binarySortableSerializeWrite); + serializedMultiKeySeries.init(bigTableKeyTypeInfos, bigTableKeyColumnMap); needCommonSetup = false; } @@ -121,12 +128,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Multi-Key hash map information for this specialized class. */ - hashMap = (VectorMapJoinBytesHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner join. @@ -137,9 +145,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -153,233 +161,85 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Multi-Key specific declarations. - */ - - // None. - - /* - * Multi-Key check for repeating. - */ - - // If all BigTable input columns to key expressions are isRepeating, then - // calculate key once; lookup once. - boolean allKeyInputColumnsRepeating; - if (bigTableKeyColumnMap.length == 0) { - allKeyInputColumnsRepeating = false; - } else { - allKeyInputColumnsRepeating = true; - for (int i = 0; i < bigTableKeyColumnMap.length; i++) { - if (!batch.cols[bigTableKeyColumnMap[i]].isRepeating) { - allKeyInputColumnsRepeating = false; - break; - } - } - } + serializedMultiKeySeries.processBatch(batch); - if (allKeyInputColumnsRepeating) { - - /* - * Repeating. - */ + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMapResultCount = 0; - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + do { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - /* - * Multi-Key specific repeated lookup. - */ + // NOTE: Any null column in the key for inner join is a non-match. + if (serializedMultiKeySeries.getCurrentHasAnyNulls()) { - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, 0); - JoinUtil.JoinResult joinResult; - if (keyVectorSerializeWrite.getHasAnyNulls()) { - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - byte[] keyBytes = currentKeyOutput.getData(); - int keyLength = currentKeyOutput.getLength(); - joinResult = hashMap.lookup(keyBytes, 0, keyLength, hashMapResults[0]); - } + // CONSIDER: Add support for NullSafe option. - /* - * Common repeated join result processing. - */ + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishInnerRepeated(batch, joinResult, hashMapResults[0]); - } else { + } else { - /* - * NOT Repeating. - */ + hashMap.hashMapLookup( + serializedMultiKeySeries.getSerializedBytes(), + serializedMultiKeySeries.getSerializedStart(), + serializedMultiKeySeries.getSerializedLength(), + serializedMultiKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - /* - * Multi-Key specific variables. + * Common inner join result processing. */ - Output temp; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Multi-Key get key. - */ - - // Generate binary sortable key for current row in vectorized row batch. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, batchIndex); - boolean isAnyNull = keyVectorSerializeWrite.getHasAnyNulls(); - - /* - * Equal key series checking. - */ - - if (isAnyNull || !haveSaveKey || !saveKeyOutput.arraysEquals(currentKeyOutput)) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isAnyNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Multi-Key specific save key. - */ - - temp = saveKeyOutput; - saveKeyOutput = currentKeyOutput; - currentKeyOutput = temp; - - /* - * Multi-Key specific lookup key. - */ - - byte[] keyBytes = saveKeyOutput.getData(); - int keyLength = saveKeyOutput.getLength(); - saveJoinResult = hashMap.lookup(keyBytes, 0, keyLength, hashMapResults[hashMapResultCount]); - } - - /* - * Common inner join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } + switch (lookupResult) { + case MATCH: + matchLogicalIndices[matchSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; + hashMapResultCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; + hashMapResultCount++; + spilledRowCounter += serializedMultiKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); + if (!serializedMultiKeySeries.next()) { + break; } - - finishInner(batch, - allMatchCount, equalKeySeriesCount, spillCount, hashMapResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishInner(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerStringOperator.java index b2711c3..3763f5c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerStringOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinInnerStringOperator.java @@ -23,19 +23,20 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column String hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytes; // Single-Column String specific imports. -import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; /* * Specialized class for doing a vectorized map join that is an inner join on a Single-Column String @@ -44,8 +45,17 @@ public class VectorMapJoinInnerStringOperator extends VectorMapJoinInnerGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerStringOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinInnerStringOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -54,7 +64,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Single-Column String specific members. @@ -63,6 +73,9 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + // The object that determines equal key series. + private transient VectorKeySeriesBytes bytesKeySeries; + //--------------------------------------------------------------------------- // Pass-thru constructors. // @@ -97,6 +110,8 @@ public void process(Object row, int tag) throws HiveException { singleJoinColumn = bigTableKeyColumnMap[0]; + bytesKeySeries = new VectorKeySeriesBytes(singleJoinColumn); + needCommonSetup = false; } @@ -108,12 +123,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column String hash map information for this specialized class. */ - hashMap = (VectorMapJoinBytesHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an inner join. @@ -124,9 +140,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -140,222 +156,90 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Single-Column String specific declarations. - */ + bytesKeySeries.processBatch(batch); + // LOG.info(CLASS_NAME + " seriesCount " + bytesKeySeries.getSeriesCount() + " nonNullKeyCount " + + // bytesKeySeries.getNonNullKeyCount()); - // The one join column for this specialized class. - BytesColumnVector joinColVector = (BytesColumnVector) batch.cols[singleJoinColumn]; - byte[][] vector = joinColVector.vector; - int[] start = joinColVector.start; - int[] length = joinColVector.length; + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMapResultCount = 0; - /* - * Single-Column String check for repeating. - */ + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + do { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + if (bytesKeySeries.getCurrentIsAllNull()) { - if (allKeyInputColumnsRepeating) { + // CONSIDER: Add support for NullSafe option. - /* - * Repeating. - */ - - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Single-Column String specific repeated lookup. - */ - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - byte[] keyBytes = vector[0]; - int keyStart = start[0]; - int keyLength = length[0]; - joinResult = hashMap.lookup(keyBytes, keyStart, keyLength, hashMapResults[0]); - } - /* - * Common repeated join result processing. - */ + hashMap.hashMapLookup( + bytesKeySeries.getCurrentBytes(), + bytesKeySeries.getCurrentStart(), + bytesKeySeries.getCurrentLength(), + bytesKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishInnerRepeated(batch, joinResult, hashMapResults[0]); - } else { + // LOG.info(CLASS_NAME + " " + + // VectorizedBatchUtil.displayBytes(bytesKeySeries.currentBytes, bytesKeySeries.currentStart, + // bytesKeySeries.currentLength) + " hashCode " + Integer.toHexString(bytesKeySeries.getCurrentHashCode()) + + // " lookupResult " + lookupResult.name()); - /* - * NOT Repeating. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - /* - * Single-Column String specific variables. + * Common inner join result processing. */ - - int saveKeyBatchIndex = -1; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column String get key. - */ - - // Implicit -- use batchIndex. - boolean isNull = !joinColVector.noNulls && joinColVector.isNull[batchIndex]; - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || - StringExpr.equal(vector[saveKeyBatchIndex], start[saveKeyBatchIndex], length[saveKeyBatchIndex], - vector[batchIndex], start[batchIndex], length[batchIndex]) == false) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column String specific save key. - */ - - saveKeyBatchIndex = batchIndex; - - /* - * Single-Column String specific lookup key. - */ - - byte[] keyBytes = vector[batchIndex]; - int keyStart = start[batchIndex]; - int keyLength = length[batchIndex]; - saveJoinResult = hashMap.lookup(keyBytes, keyStart, keyLength, hashMapResults[hashMapResultCount]); - } - - /* - * Common inner join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } + switch (lookupResult) { + case MATCH: + matchLogicalIndices[matchSeriesCount] = bytesKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; + hashMapResultCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = bytesKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; + hashMapResultCount++; + spilledRowCounter += bytesKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } + if (!bytesKeySeries.next()) { + break; } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); - } - - finishInner(batch, - allMatchCount, equalKeySeriesCount, spillCount, hashMapResultCount); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishInner(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiGenerateResultOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiGenerateResultOperator.java index d1d6c42..b7507bc 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiGenerateResultOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiGenerateResultOperator.java @@ -22,13 +22,9 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; -import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; @@ -58,17 +54,22 @@ // An array of hash set results so we can do lookups on the whole batch before output result // generation. - protected transient VectorMapJoinHashSetResult hashSetResults[]; + protected transient MapJoinHashSetResult hashSetResults[]; - // Pre-allocated member for storing the (physical) batch index of matching row (single- or - // multi-small-table-valued) indexes during a process call. - protected transient int[] allMatchs; + // Pre-allocated member for storing the batch indices of the matched rows. + protected transient int[] matchSelected; - // Pre-allocated member for storing the (physical) batch index of rows that need to be spilled. - protected transient int[] spills; + // Pre-allocated member for storing the new logical batch index (within newSelected) and + // series count of rows that matched. + protected transient int[] matchLogicalIndices; + protected transient int[] matchDuplicateCounts; - // Pre-allocated member for storing index into the hashSetResults for each spilled row. - protected transient int[] spillHashMapResultIndices; + // Pre-allocated member for storing the logical batch index and series count of rows that spilled. + protected transient int[] spillLogicalIndices; + protected transient int[] spillDuplicateCounts; + + // Pre-allocated member for storing the reference to the hash map results of rows that spilled. + protected transient MapJoinHashSetResult[] spillHashSetResults; public VectorMapJoinLeftSemiGenerateResultOperator() { super(); @@ -86,17 +87,19 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { super.commonSetup(batch); // Semi join specific. - VectorMapJoinHashSet baseHashSet = (VectorMapJoinHashSet) vectorMapJoinHashTable; - - hashSetResults = new VectorMapJoinHashSetResult[batch.DEFAULT_SIZE]; + hashSetResults = new MapJoinHashSetResult[batch.DEFAULT_SIZE]; for (int i = 0; i < hashSetResults.length; i++) { - hashSetResults[i] = baseHashSet.createHashSetResult(); + hashSetResults[i] = vectormapJoinHashTableFactory.createHashSetResult(); } - allMatchs = new int[batch.DEFAULT_SIZE]; + matchSelected = new int[batch.DEFAULT_SIZE]; - spills = new int[batch.DEFAULT_SIZE]; - spillHashMapResultIndices = new int[batch.DEFAULT_SIZE]; + matchLogicalIndices = new int[batch.DEFAULT_SIZE]; + matchDuplicateCounts = new int[batch.DEFAULT_SIZE]; + + spillLogicalIndices = new int[batch.DEFAULT_SIZE]; + spillDuplicateCounts = new int[batch.DEFAULT_SIZE]; + spillHashSetResults = new MapJoinHashSetResult[batch.DEFAULT_SIZE]; } //----------------------------------------------------------------------------------------------- @@ -111,32 +114,47 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { * @param batch * The big table batch with any matching and any non matching rows both as * selected in use. - * @param allMatchCount - * Number of matches in allMatchs. - * @param spillCount - * Number of spills in spills. - * @param hashTableResults - * The array of all hash table results for the batch. We need the - * VectorMapJoinHashTableResult for the spill information. + * @param matchSeriesCount + * Number of matches in the match* arrays. + * @param matchSelectedCount + * The selected count in matchSelected. + * @param spillSeriesCount + * Number of spills in spill* arrays. */ - protected void finishLeftSemi(VectorizedRowBatch batch, - int allMatchCount, int spillCount, - VectorMapJoinHashTableResult[] hashTableResults) throws HiveException, IOException { - - // Get rid of spills before we start modifying the batch. - if (spillCount > 0) { - spillHashMapBatch(batch, hashTableResults, - spills, spillHashMapResultIndices, spillCount); + protected void finishLeftSemi(VectorizedRowBatch batch, int matchSeriesCount, + int spillSeriesCount) throws HiveException, IOException { + + final int selectedSize = batch.size; + boolean selectedInUse = batch.selectedInUse; + int[] selected = batch.selected; + + // Dump out the spill rows now. + if (spillSeriesCount > 0) { + + spillHashMapBatch(batch, spillLogicalIndices, spillDuplicateCounts, spillHashSetResults, + spillSeriesCount, selectedInUse, selected, selectedSize); + + if (spillSeriesCount == selectedSize) { + batch.size = 0; + batch.selectedInUse = false; + return; + } } + int matchSelectedCount = + flattenLogicalSeriesIntoSelected( + selectedInUse, selected, selectedSize, + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + matchSelected); + /* * Optimize by running value expressions only over the matched rows. */ - if (allMatchCount > 0 && bigTableValueExpressions != null) { - performValueExpressions(batch, allMatchs, allMatchCount); + if (matchSeriesCount > 0 && bigTableValueExpressions != null) { + performValueExpressions(batch, matchSelected, matchSelectedCount); } - int numSel = generateHashSetResults(batch, allMatchs, allMatchCount); + int numSel = generateHashSetResults(batch, matchSelected, matchSelectedCount); batch.size = numSel; batch.selectedInUse = true; } @@ -169,57 +187,4 @@ private int generateHashSetResults(VectorizedRowBatch batch, return numSel; } - - /** - * Generate the left semi join output results for one vectorized row batch with a repeated key. - * - * @param batch - * The big table batch whose repeated key matches. - */ - protected int generateHashSetResultRepeatedAll(VectorizedRowBatch batch) throws HiveException { - - if (batch.selectedInUse) { - // The selected array is already filled in as we want it. - } else { - int[] selected = batch.selected; - for (int i = 0; i < batch.size; i++) { - selected[i] = i; - } - batch.selectedInUse = true; - } - - return batch.size; - } - - protected void finishLeftSemiRepeated(VectorizedRowBatch batch, JoinUtil.JoinResult joinResult, - VectorMapJoinHashTableResult hashSetResult) throws HiveException, IOException { - - switch (joinResult) { - case MATCH: - - if (bigTableValueExpressions != null) { - // Run our value expressions over whole batch. - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } - } - - // Generate special repeated case. - int numSel = generateHashSetResultRepeatedAll(batch); - batch.size = numSel; - batch.selectedInUse = true; - break; - - case SPILL: - // Whole batch is spilled. - spillBatchRepeated(batch, (VectorMapJoinHashTableResult) hashSetResult); - batch.size = 0; - break; - - case NOMATCH: - // No match for entire batch. - batch.size = 0; - break; - } - } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiLongOperator.java index 4b8ab58..9915b33 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiLongOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiLongOperator.java @@ -23,19 +23,19 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column Long hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashSet; - -// Single-Column Long specific imports. -import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; +import org.apache.hive.common.util.HashCodeUtil; /* * Specialized class for doing a vectorized map join that is an left semi join on a Single-Column Long @@ -44,8 +44,17 @@ public class VectorMapJoinLeftSemiLongOperator extends VectorMapJoinLeftSemiGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinLeftSemiLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -54,7 +63,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinLongHashSet hashSet; + private transient MapJoinHashTableFind hashSet; //--------------------------------------------------------------------------- // Single-Column Long specific members. @@ -67,6 +76,10 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + private transient PrimitiveTypeInfo singleJoinColumnPrimitiveTypeInfo; + + // The object that determines equal key series. + private transient VectorKeySeriesLong longKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -101,6 +114,10 @@ public void process(Object row, int tag) throws HiveException { */ singleJoinColumn = bigTableKeyColumnMap[0]; + singleJoinColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) bigTableKeyTypeInfos[0]; + + longKeySeries = + new VectorKeySeriesLong(singleJoinColumn, singleJoinColumnPrimitiveTypeInfo); needCommonSetup = false; } @@ -113,7 +130,7 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column Long hash set information for this specialized class. */ - hashSet = (VectorMapJoinLongHashSet) vectorMapJoinHashTable; + hashSet = vectorMapJoinHashTableFind; useMinMax = hashSet.useMinMax(); if (useMinMax) { min = hashSet.min(); @@ -124,6 +141,7 @@ public void process(Object row, int tag) throws HiveException { } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an left semi join. @@ -135,9 +153,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -151,222 +169,93 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Single-Column Long specific declarations. - */ - // The one join column for this specialized class. - LongColumnVector joinColVector = (LongColumnVector) batch.cols[singleJoinColumn]; - long[] vector = joinColVector.vector; + longKeySeries.processBatch(batch); + // LOG.info(CLASS_NAME + " seriesCount " + longKeySeries.getSeriesCount() + " nonNullKeyCount " + + // longKeySeries.getNonNullKeyCount()); - /* - * Single-Column Long check for repeating. - */ + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashSetResultCount = 0; - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + MapJoinHashSetResult hashSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + long key; + do { + // Use the next hash set result entry. + hashSetResult = hashSetResults[hashSetResultCount]; - if (allKeyInputColumnsRepeating) { + if (longKeySeries.getCurrentIsAllNull()) { - /* - * Repeating. - */ + // CONSIDER: Add support for NullSafe option. - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Single-Column Long specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - long key = vector[0]; + + key = longKeySeries.getCurrentKey(); if (useMinMax && (key < min || key > max)) { // Out of range for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; } else { - joinResult = hashSet.contains(key, hashSetResults[0]); + hashSet.hashSetContains( + key, + longKeySeries.getCurrentHashCode(), + hashSetResult); + containsResult = hashSetResult.getMapJoinResult(); } - } - - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishLeftSemiRepeated(batch, joinResult, hashSetResults[0]); - } else { - /* - * NOT Repeating. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashSetResultCount = 0; - int allMatchCount = 0; - int spillCount = 0; + // LOG.info(CLASS_NAME + " key " + key + + // " hashCode " + Integer.toHexString(longKeySeries.getCurrentHashCode()) + + // " containsResult " + containsResult.name()); /* - * Single-Column Long specific variables. + * Common inner join result processing. */ - - long saveKey = 0; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column Long get key. - */ - - long currentKey; - boolean isNull; - if (!joinColVector.noNulls && joinColVector.isNull[batchIndex]) { - currentKey = 0; - isNull = true; - } else { - currentKey = vector[batchIndex]; - isNull = false; - } - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || currentKey != saveKey) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column Long specific save key. - */ - - saveKey = currentKey; - - /* - * Single-Column Long specific lookup key. - */ - - if (useMinMax && (currentKey < min || currentKey > max)) { - // Key out of range for whole hash table. - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - } else { - saveJoinResult = hashSet.contains(currentKey, hashSetResults[hashSetResultCount]); - } - } - - /* - * Common left-semi join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } + switch (containsResult) { + case MATCH: + // We have extracted the existence from the hash set result, so we don't keep it. + matchLogicalIndices[matchSeriesCount] = longKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + matchSeriesCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = longKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + spillHashSetResults[spillSeriesCount] = hashSetResult; + spillSeriesCount++; + hashSetResultCount++; + spilledRowCounter += longKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashSetResults, 0, hashSetResultCount))); + if (!longKeySeries.next()) { + break; } - - finishLeftSemi(batch, - allMatchCount, spillCount, - (VectorMapJoinHashTableResult[]) hashSetResults); + } while (true); + + if (isLogDebugEnabled) { + LOG.info(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishLeftSemi(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiMultiKeyOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiMultiKeyOperator.java index bdf7901..8da25fe 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiMultiKeyOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiMultiKeyOperator.java @@ -23,20 +23,17 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesMultiFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; -// Multi-Key hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet; - // Multi-Key specific imports. -import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; -import org.apache.hadoop.hive.serde2.ByteStream.Output; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; /* @@ -46,8 +43,17 @@ public class VectorMapJoinLeftSemiMultiKeyOperator extends VectorMapJoinLeftSemiGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinLeftSemiMultiKeyOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -56,21 +62,17 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashSet hashSet; + private transient MapJoinHashTableFind hashSet; //--------------------------------------------------------------------------- // Multi-Key specific members. // - // Object that can take a set of columns in row in a vectorized row batch and serialized it. - // Known to not have any nulls. - private transient VectorSerializeRow keyVectorSerializeWrite; + // Binary sortable multi-key serializer. + private transient BinarySortableSerializeWrite binarySortableSerializeWrite; - // The BinarySortable serialization of the current key. - private transient Output currentKeyOutput; - - // The BinarySortable serialization of the saved key for a possible series of equal keys. - private transient Output saveKeyOutput; + // The object that determines equal key series. + private transient VectorKeySeriesMultiFast serializedMultiKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -104,12 +106,15 @@ public void process(Object row, int tag) throws HiveException { * Initialize Multi-Key members for this specialized class. */ - keyVectorSerializeWrite = new VectorSerializeRow( - new BinarySortableSerializeWrite(bigTableKeyColumnMap.length)); - keyVectorSerializeWrite.init(bigTableKeyTypeNames, bigTableKeyColumnMap); + binarySortableSerializeWrite = + new BinarySortableSerializeWrite(bigTableKeyColumnMap.length); - currentKeyOutput = new Output(); - saveKeyOutput = new Output(); + // For Multi-Key the Fast hash table computes the serialized key's hash code, + // so we don't use VectorKeySeriesMulti's hash code. + serializedMultiKeySeries = + new VectorKeySeriesMultiFast( + binarySortableSerializeWrite); + serializedMultiKeySeries.init(bigTableKeyTypeInfos, bigTableKeyColumnMap); needCommonSetup = false; } @@ -122,12 +127,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Multi-Key hash set information for this specialized class. */ - hashSet = (VectorMapJoinBytesHashSet) vectorMapJoinHashTable; + hashSet = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an left semi join. @@ -139,9 +145,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -155,228 +161,83 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Multi-Key specific declarations. - */ - - // None. - - /* - * Multi-Key Long check for repeating. - */ - - // If all BigTable input columns to key expressions are isRepeating, then - // calculate key once; lookup once. - boolean allKeyInputColumnsRepeating; - if (bigTableKeyColumnMap.length == 0) { - allKeyInputColumnsRepeating = false; - } else { - allKeyInputColumnsRepeating = true; - for (int i = 0; i < bigTableKeyColumnMap.length; i++) { - if (!batch.cols[bigTableKeyColumnMap[i]].isRepeating) { - allKeyInputColumnsRepeating = false; - break; - } - } - } - if (allKeyInputColumnsRepeating) { + serializedMultiKeySeries.processBatch(batch); - /* - * Repeating. - */ + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashSetResultCount = 0; - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + MapJoinHashSetResult hashSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + do { + // Use the next hash set result entry. + hashSetResult = hashSetResults[hashSetResultCount]; - /* - * Multi-Key specific repeated lookup. - */ + // NOTE: Any null column in the key for inner join is a non-match. + if (serializedMultiKeySeries.getCurrentHasAnyNulls()) { - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, 0); - JoinUtil.JoinResult joinResult; - if (keyVectorSerializeWrite.getHasAnyNulls()) { - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - byte[] keyBytes = currentKeyOutput.getData(); - int keyLength = currentKeyOutput.getLength(); - // LOG.debug(CLASS_NAME + " processOp all " + displayBytes(keyBytes, 0, keyLength)); - joinResult = hashSet.contains(keyBytes, 0, keyLength, hashSetResults[0]); - } + // CONSIDER: Add support for NullSafe option. - /* - * Common repeated join result processing. - */ + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishLeftSemiRepeated(batch, joinResult, hashSetResults[0]); - } else { + } else { - /* - * NOT Repeating. - */ + hashSet.hashSetContains( + serializedMultiKeySeries.getSerializedBytes(), + serializedMultiKeySeries.getSerializedStart(), + serializedMultiKeySeries.getSerializedLength(), + serializedMultiKeySeries.getCurrentHashCode(), + hashSetResult); + containsResult = hashSetResult.getMapJoinResult(); - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashSetResultCount = 0; - int allMatchCount = 0; - int spillCount = 0; - /* - * Multi-Key specific variables. + * Common inner join result processing. */ - - Output temp; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Multi-Key get key. - */ - - // Generate binary sortable key for current row in vectorized row batch. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, batchIndex); - boolean isAnyNull = keyVectorSerializeWrite.getHasAnyNulls(); - - // LOG.debug(CLASS_NAME + " currentKey " + - // VectorizedBatchUtil.displayBytes(currentKeyOutput.getData(), 0, currentKeyOutput.getLength())); - - /* - * Equal key series checking. - */ - - if (isAnyNull || !haveSaveKey || !saveKeyOutput.arraysEquals(currentKeyOutput)) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isAnyNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Multi-Key specific save key and lookup. - */ - - temp = saveKeyOutput; - saveKeyOutput = currentKeyOutput; - currentKeyOutput = temp; - - /* - * Multi-key specific lookup key. - */ - - byte[] keyBytes = saveKeyOutput.getData(); - int keyLength = saveKeyOutput.getLength(); - saveJoinResult = hashSet.contains(keyBytes, 0, keyLength, hashSetResults[hashSetResultCount]); - } - - /* - * Common left-semi join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } + switch (containsResult) { + case MATCH: + // We have extracted the existence from the hash set result, so we don't keep it. + matchLogicalIndices[matchSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + matchSeriesCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + spillHashSetResults[spillSeriesCount] = hashSetResult; + spillSeriesCount++; + hashSetResultCount++; + spilledRowCounter += serializedMultiKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } + if (!serializedMultiKeySeries.next()) { + break; } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashSetResults, 0, hashSetResultCount))); - } - - finishLeftSemi(batch, - allMatchCount, spillCount, - (VectorMapJoinHashTableResult[]) hashSetResults); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishLeftSemi(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiStringOperator.java index a8d3459..cd3d8ea 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiStringOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinLeftSemiStringOperator.java @@ -23,20 +23,17 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column String hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet; - -// Single-Column String specific imports. -import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytes; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.plan.OperatorDesc; /* * Specialized class for doing a vectorized map join that is an left semi join on a Single-Column String @@ -45,8 +42,17 @@ public class VectorMapJoinLeftSemiStringOperator extends VectorMapJoinLeftSemiGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinInnerBigOnlyLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinLeftSemiStringOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -55,7 +61,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashSet hashSet; + private transient MapJoinHashTableFind hashSet; //--------------------------------------------------------------------------- // Single-Column String specific members. @@ -64,6 +70,9 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + // The object that determines equal key series. + private transient VectorKeySeriesBytes bytesKeySeries; + //--------------------------------------------------------------------------- // Pass-thru constructors. // @@ -98,6 +107,8 @@ public void process(Object row, int tag) throws HiveException { singleJoinColumn = bigTableKeyColumnMap[0]; + bytesKeySeries = new VectorKeySeriesBytes(singleJoinColumn); + needCommonSetup = false; } @@ -109,12 +120,13 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column String hash set information for this specialized class. */ - hashSet = (VectorMapJoinBytesHashSet) vectorMapJoinHashTable; + hashSet = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; // Do the per-batch setup for an left semi join. @@ -126,9 +138,9 @@ public void process(Object row, int tag) throws HiveException { ve.evaluate(batch); } - final int inputLogicalSize = batch.size; + final int filteredSize = batch.size; - if (inputLogicalSize == 0) { + if (filteredSize == 0) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } @@ -142,214 +154,79 @@ public void process(Object row, int tag) throws HiveException { } } - /* - * Single-Column String specific declarations. - */ - - // The one join column for this specialized class. - BytesColumnVector joinColVector = (BytesColumnVector) batch.cols[singleJoinColumn]; - byte[][] vector = joinColVector.vector; - int[] start = joinColVector.start; - int[] length = joinColVector.length; + bytesKeySeries.processBatch(batch); - /* - * Single-Column Long check for repeating. - */ + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashSetResultCount = 0; - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + MapJoinHashSetResult hashSetResult; + MapJoinHashTableResult.MapJoinResult containsResult; + do { + // Use the next hash set result entry. + hashSetResult = hashSetResults[hashSetResultCount]; - if (allKeyInputColumnsRepeating) { + if (bytesKeySeries.getCurrentIsAllNull()) { - /* - * Repeating. - */ + // CONSIDER: Add support for NullSafe option. - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + containsResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; - /* - * Single-Column String specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (!joinColVector.noNulls && joinColVector.isNull[0]) { - joinResult = JoinUtil.JoinResult.NOMATCH; } else { - byte[] keyBytes = vector[0]; - int keyStart = start[0]; - int keyLength = length[0]; - joinResult = hashSet.contains(keyBytes, keyStart, keyLength, hashSetResults[0]); - } - - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); + hashSet.hashSetContains( + bytesKeySeries.getCurrentBytes(), + bytesKeySeries.getCurrentStart(), + bytesKeySeries.getCurrentLength(), + bytesKeySeries.getCurrentHashCode(), + hashSetResult); + containsResult = hashSetResult.getMapJoinResult(); } - finishLeftSemiRepeated(batch, joinResult, hashSetResults[0]); - } else { /* - * NOT Repeating. + * Common inner join result processing. */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); + switch (containsResult) { + case MATCH: + // We have extracted the existence from the hash set result, so we don't keep it. + matchLogicalIndices[matchSeriesCount] = bytesKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + matchSeriesCount++; + break; + + case SPILL: + spillLogicalIndices[spillSeriesCount] = bytesKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + spillHashSetResults[spillSeriesCount] = hashSetResult; + spillSeriesCount++; + hashSetResultCount++; + spilledRowCounter += bytesKeySeries.getCurrentDuplicateCount(); + break; + + case NO_MATCH: + break; + + default: + throw new RuntimeException("Unexpected contains result " + containsResult.name()); } - // We remember any matching rows in matchs / matchSize. At the end of the loop, - // selected / batch.size will represent both matching and non-matching rows for outer join. - // Only deferred rows will have been removed from selected. - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashSetResultCount = 0; - int allMatchCount = 0; - int spillCount = 0; - - /* - * Single-Column String specific variables. - */ - - int saveKeyBatchIndex = -1; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < inputLogicalSize; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - /* - * Single-Column String get key. - */ - - // Implicit -- use batchIndex. - boolean isNull = !joinColVector.noNulls && joinColVector.isNull[batchIndex]; - - /* - * Equal key series checking. - */ - - if (isNull || !haveSaveKey || - StringExpr.equal(vector[saveKeyBatchIndex], start[saveKeyBatchIndex], length[saveKeyBatchIndex], - vector[batchIndex], start[batchIndex], length[batchIndex]) == false) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isNull) { - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - haveSaveKey = false; - } else { - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column String specific save key and lookup. - */ - - saveKeyBatchIndex = batchIndex; - - /* - * Single-Column String specific lookup key. - */ - - byte[] keyBytes = vector[batchIndex]; - int keyStart = start[batchIndex]; - int keyLength = length[batchIndex]; - saveJoinResult = hashSet.contains(keyBytes, keyStart, keyLength, hashSetResults[hashSetResultCount]); - } - - /* - * Common left-semi join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashSetResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } + if (!bytesKeySeries.next()) { + break; } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { - case MATCH: - // We have extracted the existence from the hash set result, so we don't keep it. - break; - case SPILL: - // We keep the hash set result for its spill information. - hashSetResultCount++; - break; - case NOMATCH: - break; - } - } - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + - " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashSetResults, 0, hashSetResultCount))); - } - - finishLeftSemi(batch, - allMatchCount, spillCount, - (VectorMapJoinHashTableResult[]) hashSetResults); + } while (true); + + if (isLogDebugEnabled) { + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + + " filteredSize " + filteredSize + + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } + finishLeftSemi(batch, matchSeriesCount, spillSeriesCount); + if (batch.size > 0) { // Forward any remaining selected rows. forwardBigTableBatch(batch); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterGenerateResultOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterGenerateResultOperator.java index 5a88784..60194cb 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterGenerateResultOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterGenerateResultOperator.java @@ -19,19 +19,19 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin; import java.io.IOException; + import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; + +import java.util.Arrays; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; -import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; /** * This class has methods for generating vectorized join results for outer joins. @@ -66,50 +66,44 @@ // Outer join specific members. // + protected transient long outerJoinNullKeyCounter; + protected transient long outerJoinFilteredOutCounter; + // An array of hash map results so we can do lookups on the whole batch before output result // generation. - protected transient VectorMapJoinHashMapResult hashMapResults[]; + protected transient MapJoinHashMapResult hashMapResults[]; - // Pre-allocated member for remembering the big table's selected array at the beginning of - // the process method before applying any filter. For outer join we need to remember which - // rows did not match since they will appear the in outer join result with NULLs for the - // small table. - protected transient int[] inputSelected; + // For outer join, we must some how retain our input row selection before ON expression + // filtering and before hash table matching so we can generate results for all rows (matching + // and non matching) later. Since we are knocking rows out in different phases, we use a + // copy of the selected array. + protected int inputLogicalSize; + protected int filteredSize; + protected boolean inputSelectedInUse; - // Pre-allocated member for storing the (physical) batch index of matching row (single- or - // multi-small-table-valued) indexes during a process call. - protected transient int[] allMatchs; + // Pre-allocated member for storing the batch indices of the input batch rows. + protected transient int[] inputSelected; - /* - * Pre-allocated members for storing information equal key series for small-table matches. - * - * ~HashMapResultIndices - * Index into the hashMapResults array for the match. - * ~AllMatchIndices - * (Logical) indices into allMatchs to the first row of a match of a - * possible series of duplicate keys. - * ~IsSingleValue - * Whether there is 1 or multiple small table values. - * ~DuplicateCounts - * The duplicate count for each matched key. - * - */ - protected transient int[] equalKeySeriesHashMapResultIndices; - protected transient int[] equalKeySeriesAllMatchIndices; - protected transient boolean[] equalKeySeriesIsSingleValue; - protected transient int[] equalKeySeriesDuplicateCounts; + // Pre-allocated member for storing the logical batch index (within input batch), + // series count of rows, and hash map results that matched. + protected transient int[] matchLogicalIndices; + protected transient int[] matchDuplicateCounts; + protected transient boolean[] matchIsSingleValue; + protected transient MapJoinHashMapResult[] matchHashMapResults; - // Pre-allocated member for storing the (physical) batch index of rows that need to be spilled. - protected transient int[] spills; + // Pre-allocated member for storing the logical batch index and series count of rows that spilled. + protected transient int[] spillLogicalIndices; + protected transient int[] spillDuplicateCounts; - // Pre-allocated member for storing index into the hashSetResults for each spilled row. - protected transient int[] spillHashMapResultIndices; + // Pre-allocated member for storing the reference to the hash map results of rows that spilled. + protected transient MapJoinHashMapResult[] spillHashMapResults; - // Pre-allocated member for storing any non-spills, non-matches, or merged row indexes during a - // process method call. - protected transient int[] nonSpills; - protected transient int[] noMatchs; - protected transient int[] merged; + // Pre-allocated member for storing any non-spills, matches, and non-matches batch indexes during + // a process method call. + protected transient int[] nonSpillSelected; + protected transient int[] matchSelected; + protected transient int[] nonMatchSelected; + protected transient int[] resultSelected; public VectorMapJoinOuterGenerateResultOperator() { super(); @@ -126,29 +120,30 @@ public VectorMapJoinOuterGenerateResultOperator(VectorizationContext vContext, O protected void commonSetup(VectorizedRowBatch batch) throws HiveException { super.commonSetup(batch); - // Outer join specific. - VectorMapJoinHashMap baseHashMap = (VectorMapJoinHashMap) vectorMapJoinHashTable; + outerJoinNullKeyCounter = 0; + outerJoinFilteredOutCounter = 0; - hashMapResults = new VectorMapJoinHashMapResult[batch.DEFAULT_SIZE]; + // Outer join specific. + hashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; for (int i = 0; i < hashMapResults.length; i++) { - hashMapResults[i] = baseHashMap.createHashMapResult(); + hashMapResults[i] = vectormapJoinHashTableFactory.createHashMapResult(); } inputSelected = new int[batch.DEFAULT_SIZE]; - allMatchs = new int[batch.DEFAULT_SIZE]; - - equalKeySeriesHashMapResultIndices = new int[batch.DEFAULT_SIZE]; - equalKeySeriesAllMatchIndices = new int[batch.DEFAULT_SIZE]; - equalKeySeriesIsSingleValue = new boolean[batch.DEFAULT_SIZE]; - equalKeySeriesDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchLogicalIndices = new int[batch.DEFAULT_SIZE]; + matchDuplicateCounts = new int[batch.DEFAULT_SIZE]; + matchIsSingleValue = new boolean[batch.DEFAULT_SIZE]; + matchHashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; - spills = new int[batch.DEFAULT_SIZE]; - spillHashMapResultIndices = new int[batch.DEFAULT_SIZE]; + spillLogicalIndices = new int[batch.DEFAULT_SIZE]; + spillDuplicateCounts = new int[batch.DEFAULT_SIZE]; + spillHashMapResults = new MapJoinHashMapResult[batch.DEFAULT_SIZE]; - nonSpills = new int[batch.DEFAULT_SIZE]; - noMatchs = new int[batch.DEFAULT_SIZE]; - merged = new int[batch.DEFAULT_SIZE]; + nonSpillSelected = new int[batch.DEFAULT_SIZE]; + matchSelected = new int[batch.DEFAULT_SIZE]; + nonMatchSelected = new int[batch.DEFAULT_SIZE]; + resultSelected = new int[batch.DEFAULT_SIZE]; } @@ -161,7 +156,20 @@ protected void commonSetup(VectorizedRowBatch batch) throws HiveException { /** * Do the per-batch setup for an outer join. */ - protected void outerPerBatchSetup(VectorizedRowBatch batch) { + protected boolean outerPerBatchSetup(VectorizedRowBatch batch) { + + inputLogicalSize = batch.size; + if (inputLogicalSize == 0) { + return false; + } + + inputSelectedInUse = batch.selectedInUse; + if (inputSelectedInUse) { + // if (!verifyMonotonicallyIncreasing(batch.selected, batch.size)) { + // throw new HiveException("batch.selected is not in sort order and unique"); + // } + System.arraycopy(batch.selected, 0, inputSelected, 0, inputLogicalSize); + } // For join operators that can generate small table results, reset their // (target) scratch columns. @@ -175,373 +183,208 @@ protected void outerPerBatchSetup(VectorizedRowBatch batch) { ColumnVector bigTableOuterKeyColumn = batch.cols[column]; bigTableOuterKeyColumn.reset(); } - } - /** - * Apply the value expression to rows in the (original) input selected array. - * - * @param batch - * The vectorized row batch. - * @param inputSelectedInUse - * Whether the (original) input batch is selectedInUse. - * @param inputLogicalSize - * The (original) input batch size. - */ - private void doValueExprOnInputSelected(VectorizedRowBatch batch, - boolean inputSelectedInUse, int inputLogicalSize) { - - int saveBatchSize = batch.size; - int[] saveSelected = batch.selected; - boolean saveSelectedInUse = batch.selectedInUse; - - batch.size = inputLogicalSize; - batch.selected = inputSelected; - batch.selectedInUse = inputSelectedInUse; - - if (bigTableValueExpressions != null) { - for(VectorExpression ve: bigTableValueExpressions) { + // Filtering for outer join just removes rows available for hash table matching. + if (bigTableFilterExpressions.length > 0) { + // Since the input + for (VectorExpression ve : bigTableFilterExpressions) { ve.evaluate(batch); } + + // Since outer join outputs non matches, we do not return here on filteredSize == 0. + filteredSize = batch.size; + outerJoinFilteredOutCounter += (inputLogicalSize - filteredSize); + } else { + filteredSize = inputLogicalSize; } - batch.size = saveBatchSize; - batch.selected = saveSelected; - batch.selectedInUse = saveSelectedInUse; + /* + LOG.info(getLoggingPrefix() + " filtered size " + batch.size); + if (batch.selectedInUse) { + LOG.info(getLoggingPrefix() + " filtered selected " + Arrays.toString(Arrays.copyOfRange(batch.selected, 0, batch.size))); + } + */ + + return true; } /** - * Apply the value expression to rows specified by a selected array. + * Generate the outer join output results for one vectorized row batch. * * @param batch - * The vectorized row batch. - * @param selected - * The (physical) batch indices to apply the expression to. - * @param size - * The size of selected. + * The big table batch with any matching and any non matching rows both as + * selected in use. + * @param matchSeriesCount + * Number of match duplicate key series. + * @param spillSeriesCount + * Number of spill duplicate key series. */ - private void doValueExpr(VectorizedRowBatch batch, - int[] selected, int size) { - - int saveBatchSize = batch.size; - int[] saveSelected = batch.selected; - boolean saveSelectedInUse = batch.selectedInUse; - - batch.size = size; - batch.selected = selected; - batch.selectedInUse = true; - - if (bigTableValueExpressions != null) { - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } - } + protected void finishOuter(VectorizedRowBatch batch, int matchSeriesCount, int spillSeriesCount) + throws IOException, HiveException { - batch.size = saveBatchSize; - batch.selected = saveSelected; - batch.selectedInUse = saveSelectedInUse; - } + // LOG.info(getLoggingPrefix() + " finishOuter entering... "); - /** - * Remove (subtract) members from the input selected array and produce the results into - * a difference array. - * - * @param inputSelectedInUse - * Whether the (original) input batch is selectedInUse. - * @param inputLogicalSize - * The (original) input batch size. - * @param remove - * The indices to remove. They must all be present in input selected array. - * @param removeSize - * The size of remove. - * @param difference - * The resulting difference -- the input selected array indices not in the - * remove array. - * @return - * The resulting size of the difference array. - * @throws HiveException - */ - private int subtractFromInputSelected(boolean inputSelectedInUse, int inputLogicalSize, - int[] remove, int removeSize, int[] difference) throws HiveException { - - // if (!verifyMonotonicallyIncreasing(remove, removeSize)) { - // throw new HiveException("remove is not in sort order and unique"); - // } - - int differenceCount = 0; - - // Determine which rows are left. - int removeIndex = 0; - if (inputSelectedInUse) { - for (int i = 0; i < inputLogicalSize; i++) { - int candidateIndex = inputSelected[i]; - if (removeIndex < removeSize && candidateIndex == remove[removeIndex]) { - removeIndex++; - } else { - difference[differenceCount++] = candidateIndex; - } - } - } else { - for (int candidateIndex = 0; candidateIndex < inputLogicalSize; candidateIndex++) { - if (removeIndex < removeSize && candidateIndex == remove[removeIndex]) { - removeIndex++; - } else { - difference[differenceCount++] = candidateIndex; - } - } - } + // The match and spill information is with respect to the batch selected not inputSelected. + boolean selectedInUse = batch.selectedInUse; + int[] selected = batch.selected; + final int selectedSize = batch.size; - if (removeIndex != removeSize) { - throw new HiveException("Not all batch indices removed"); - } + // Dump out the spill rows now and determine what is left. + int nonSpillCount = 0; + if (spillSeriesCount > 0) { - // if (!verifyMonotonicallyIncreasing(difference, differenceCount)) { - // throw new HiveException("difference is not in sort order and unique"); - // } + spillHashMapBatch(batch, spillLogicalIndices, spillDuplicateCounts, spillHashMapResults, + spillSeriesCount, selectedInUse, selected, selectedSize); - return differenceCount; - } + if (spillSeriesCount == inputLogicalSize) { + batch.size = 0; + batch.selectedInUse = false; + return; + } - /** - * Remove (subtract) members from an array and produce the results into - * a difference array. - - * @param all - * The selected array containing all members. - * @param allSize - * The size of all. - * @param remove - * The indices to remove. They must all be present in input selected array. - * @param removeSize - * The size of remove. - * @param difference - * The resulting difference -- the all array indices not in the - * remove array. - * @return - * The resulting size of the difference array. - * @throws HiveException - */ - private int subtract(int[] all, int allSize, - int[] remove, int removeSize, int[] difference) throws HiveException { + // Create nonSpillSelected by not including spill batch indices. Note the spill series + // logical indices are with respect to the batch selected not inputSelected. - // if (!verifyMonotonicallyIncreasing(remove, removeSize)) { - // throw new HiveException("remove is not in sort order and unique"); - // } + nonSpillCount = + makeSelectedByRemovingSeries( + /* Remove batch indices from input batch: */ + inputSelectedInUse, inputSelected, inputLogicalSize, + /* That are in the series: */ + spillLogicalIndices, spillDuplicateCounts, spillSeriesCount, + selectedInUse, selected, selectedSize, + /* Result: */ + nonSpillSelected); - int differenceCount = 0; + } else { + // Implicitly, inputSelected is nonSpillSelected. + // LOG.info("VectorMapJoinOuterLongOperator Implicitly, inputSelected is nonSpillSelected"); - // Determine which rows are left. - int removeIndex = 0; - for (int i = 0; i < allSize; i++) { - int candidateIndex = all[i]; - if (removeIndex < removeSize && candidateIndex == remove[removeIndex]) { - removeIndex++; - } else { - difference[differenceCount++] = candidateIndex; - } } - if (removeIndex != removeSize) { - throw new HiveException("Not all batch indices removed"); - } + /* + * Optimize by running value expressions only over the matched rows. + */ + if (matchSeriesCount > 0 && bigTableValueExpressions != null) { - return differenceCount; - } + // Create matchSelected by adding the batch indices for the match logical index ranges from + // the input batch range. - /** - * Sort merge two select arrays so the resulting array is ordered by (batch) index. - * - * @param selected1 - * @param selected1Count - * @param selected2 - * @param selected2Count - * @param sortMerged - * The resulting sort merge of selected1 and selected2. - * @return - * The resulting size of the sortMerged array. - * @throws HiveException - */ - private int sortMerge(int[] selected1, int selected1Count, - int[] selected2, int selected2Count, int[] sortMerged) throws HiveException { - - // if (!verifyMonotonicallyIncreasing(selected1, selected1Count)) { - // throw new HiveException("selected1 is not in sort order and unique"); - // } - - // if (!verifyMonotonicallyIncreasing(selected2, selected2Count)) { - // throw new HiveException("selected1 is not in sort order and unique"); - // } - - - int sortMergeCount = 0; - - int selected1Index = 0; - int selected2Index = 0; - for (int i = 0; i < selected1Count + selected2Count; i++) { - if (selected1Index < selected1Count && selected2Index < selected2Count) { - if (selected1[selected1Index] < selected2[selected2Index]) { - sortMerged[sortMergeCount++] = selected1[selected1Index++]; - } else { - sortMerged[sortMergeCount++] = selected2[selected2Index++]; - } - } else if (selected1Index < selected1Count) { - sortMerged[sortMergeCount++] = selected1[selected1Index++]; - } else { - sortMerged[sortMergeCount++] = selected2[selected2Index++]; - } - } + int matchSelectedCount = + flattenLogicalSeriesIntoSelected( + selectedInUse, selected, selectedSize, + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + matchSelected); - // if (!verifyMonotonicallyIncreasing(sortMerged, sortMergeCount)) { - // throw new HiveException("sortMerged is not in sort order and unique"); - // } + // LOG.info("VectorMapJoinOuterLongOperator matchSelected selected " + Arrays.toString(Arrays.copyOfRange(matchSelected, 0, matchSelectedCount))); - return sortMergeCount; - } + performValueExpressions(batch, matchSelected, matchSelectedCount); - /** - * Generate the outer join output results for one vectorized row batch. - * - * @param batch - * The big table batch with any matching and any non matching rows both as - * selected in use. - * @param allMatchCount - * Number of matches in allMatchs. - * @param equalKeySeriesCount - * Number of single value matches. - * @param atLeastOneNonMatch - * Whether at least one row was a non-match. - * @param inputSelectedInUse - * A copy of the batch's selectedInUse flag on input to the process method. - * @param inputLogicalSize - * The batch's size on input to the process method. - * @param spillCount - * Number of spills in spills. - * @param hashMapResultCount - * Number of entries in hashMapResults. - */ - public void finishOuter(VectorizedRowBatch batch, - int allMatchCount, int equalKeySeriesCount, boolean atLeastOneNonMatch, - boolean inputSelectedInUse, int inputLogicalSize, - int spillCount, int hashMapResultCount) throws IOException, HiveException { - - // Get rid of spills before we start modifying the batch. - if (spillCount > 0) { - spillHashMapBatch(batch, (VectorMapJoinHashTableResult[]) hashMapResults, - spills, spillHashMapResultIndices, spillCount); } - int noMatchCount = 0; - if (spillCount > 0) { + int nonMatchSelectedCount = 0; + if (spillSeriesCount > 0) { - // Subtract the spills to get all match and non-match rows. - int nonSpillCount = subtractFromInputSelected( - inputSelectedInUse, inputLogicalSize, spills, spillCount, nonSpills); + // Create non match selected by not including match batch indices from non spill selected. + // Note the spill series logical indices are with respect to the batch selected not + // inputSelected. - if (isLogDebugEnabled) { - LOG.debug("finishOuter spillCount > 0" + - " nonSpills " + intArrayToRangesString(nonSpills, nonSpillCount)); - } - - // Big table value expressions apply to ALL matching and non-matching rows. - if (bigTableValueExpressions != null) { - - doValueExpr(batch, nonSpills, nonSpillCount); - - } - - if (atLeastOneNonMatch) { - noMatchCount = subtract(nonSpills, nonSpillCount, allMatchs, allMatchCount, - noMatchs); - - if (isLogDebugEnabled) { - LOG.debug("finishOuter spillCount > 0" + - " noMatchs " + intArrayToRangesString(noMatchs, noMatchCount)); - } + nonMatchSelectedCount = + makeSelectedByRemovingSeries( + /* Remove batch indices from non spill: */ + true, nonSpillSelected, nonSpillCount, + /* That are in the series: */ + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + selectedInUse, selected, selectedSize, + /* Result: */ + nonMatchSelected); - } } else { - // Run value expressions over original (whole) input batch. - doValueExprOnInputSelected(batch, inputSelectedInUse, inputLogicalSize); - - if (atLeastOneNonMatch) { - noMatchCount = subtractFromInputSelected( - inputSelectedInUse, inputLogicalSize, allMatchs, allMatchCount, noMatchs); - - if (isLogDebugEnabled) { - LOG.debug("finishOuter spillCount == 0" + - " noMatchs " + intArrayToRangesString(noMatchs, noMatchCount)); - } - } + // Create non match selected by not including match batch indices from input batch. + // Note the spill series logical indices are with respect to the batch selected + // not inputSelected. + + nonMatchSelectedCount = + makeSelectedByRemovingSeries( + /* Remove batch indices from input batch: */ + inputSelectedInUse, inputSelected, inputLogicalSize, + /* That are in the series: */ + matchLogicalIndices, matchDuplicateCounts, matchSeriesCount, + selectedInUse, selected, selectedSize, + /* Result: */ + nonMatchSelected); + + // LOG.info(getLoggingPrefix() + " finishOuter nonMatchSelectedCount " + nonMatchSelectedCount + + // " nonMatchSelected " + intArrayToRangesString(nonMatchSelected, nonMatchSelectedCount)); } - // When we generate results into the overflow batch, we may still end up with fewer rows - // in the big table batch. So, nulSel and the batch's selected array will be rebuilt with - // just the big table rows that need to be forwarded, minus any rows processed with the - // overflow batch. - if (allMatchCount > 0) { - - int numSel = 0; - for (int i = 0; i < equalKeySeriesCount; i++) { - int hashMapResultIndex = equalKeySeriesHashMapResultIndices[i]; - VectorMapJoinHashMapResult hashMapResult = hashMapResults[hashMapResultIndex]; - int allMatchesIndex = equalKeySeriesAllMatchIndices[i]; - boolean isSingleValue = equalKeySeriesIsSingleValue[i]; - int duplicateCount = equalKeySeriesDuplicateCounts[i]; - - if (isSingleValue) { - numSel = generateHashMapResultSingleValue( - batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount, numSel); - } else { - generateHashMapResultMultiValue( - batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount); - } - } + // Output matches + // ZLOG.info(getLoggingPrefix() + " finishOuter matches loop matchSeriesCount " + matchSeriesCount); - // The number of single value rows that were generated in the big table batch. - batch.size = numSel; - batch.selectedInUse = true; - - if (isLogDebugEnabled) { - LOG.debug("finishOuter allMatchCount > 0" + - " batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } + for (int i = 0; i < matchSeriesCount; i++) { + MapJoinHashMapResult hashMapResult = matchHashMapResults[i]; + int logical = matchLogicalIndices[i]; + int duplicateCount = matchDuplicateCounts[i]; + // LOG.info("VectorMapJoinOuterLongOperator output matches loop i " + i + " logical " + logical + " duplicateCount " + duplicateCount); - } else { - batch.size = 0; - } - if (noMatchCount > 0) { - if (batch.size > 0) { - - generateOuterNulls(batch, noMatchs, noMatchCount); - - // Merge noMatchs and (match) selected. - int mergeCount = sortMerge( - noMatchs, noMatchCount, batch.selected, batch.size, merged); - - if (isLogDebugEnabled) { - LOG.debug("finishOuter noMatchCount > 0 && batch.size > 0" + - " merged " + intArrayToRangesString(merged, mergeCount)); - } - - System.arraycopy(merged, 0, batch.selected, 0, mergeCount); - batch.size = mergeCount; - batch.selectedInUse = true; + // Logical indices are with respect to the batch selected not inputSelected. + if (matchIsSingleValue[i]) { + generateHashMapResultSingleValue(batch, hashMapResult, + logical, duplicateCount, selectedInUse, selected, selectedSize); } else { + generateHashMapResultMultiValue(batch, hashMapResult, + logical, duplicateCount, selectedInUse, selected, selectedSize); + } + } - // We can use the whole batch for output of no matches. + // Output non matches + if (nonMatchSelectedCount > 0) { + generateOuterNulls(batch, nonMatchSelectedCount); + } - generateOuterNullsRepeatedAll(batch); + // Create selected by not including match logical indices for multi-value small table results + // from input batch logical range, and then putting the batch indices in selected. + + int numSel; + if (spillSeriesCount > 0) { + + // Create non match selected by not including match batch indices from non spills. + // Note the spill series logical indices are with respect to the batch selected + // not inputSelected. + + numSel = + makeSelectedByRemovingMultiValues( + /* Remove batch indices from non spill: */ + true, nonSpillSelected, nonSpillCount, + /* That are in the series: */ + matchLogicalIndices, matchDuplicateCounts, matchIsSingleValue, matchSeriesCount, + selectedInUse, selected, selectedSize, + /* Result: */ + resultSelected); + } else { - System.arraycopy(noMatchs, 0, batch.selected, 0, noMatchCount); - batch.size = noMatchCount; - batch.selectedInUse = true; + // Create non match selected by not including match batch indices from input batch. + // Note the spill series logical indices are with respect to the batch selected + // not inputSelected. + + numSel = + makeSelectedByRemovingMultiValues( + /* Remove batch indices from input batch: */ + inputSelectedInUse, inputSelected, inputLogicalSize, + /* That are in the series: */ + matchLogicalIndices, matchDuplicateCounts, matchIsSingleValue, matchSeriesCount, + selectedInUse, selected, selectedSize, + /* Result: */ + resultSelected); + // LOG.info(getLoggingPrefix() + " finishOuter numSel " + numSel + + // " resultSelected " + intArrayToRangesString(resultSelected, numSel)); + } - if (isLogDebugEnabled) { - LOG.debug("finishOuter noMatchCount > 0 && batch.size == 0" + - " batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } - } + batch.selectedInUse = true; + batch.size = numSel; + if (numSel > 0) { + System.arraycopy(resultSelected, 0, selected, 0, numSel); } } @@ -558,13 +401,15 @@ public void finishOuter(VectorizedRowBatch batch, * @param noMatchSize * Number of non matches in noMatchs. */ - protected void generateOuterNulls(VectorizedRowBatch batch, int[] noMatchs, - int noMatchSize) throws IOException, HiveException { + protected void generateOuterNulls(VectorizedRowBatch batch, int noMatchSize) + throws IOException, HiveException { // Set null information in the small table results area. for (int i = 0; i < noMatchSize; i++) { - int batchIndex = noMatchs[i]; + int batchIndex = nonMatchSelected[i]; + + // LOG.info("VectorMapJoinOuterLongOperator generateOuterNulls i " + i + " batchIndex " + batchIndex); // Mark any scratch small table scratch columns that would normally receive a copy of the // key as null, too. @@ -583,163 +428,15 @@ protected void generateOuterNulls(VectorizedRowBatch batch, int[] noMatchs, } } - /** - * Generate the outer join output results for one vectorized row batch with a repeated key. - * - * Any filter expressions will apply now since hash map lookup for outer join is complete. - * - * @param batch - * The big table batch with any matching and any non matching rows both as - * selected in use. - * @param joinResult - * The hash map lookup result for the repeated key. - * @param hashMapResults - * The array of all hash map results for the batch. - * @param someRowsFilteredOut - * Whether some rows of the repeated key batch were knocked out by the filter. - * @param inputSelectedInUse - * A copy of the batch's selectedInUse flag on input to the process method. - * @param inputLogicalSize - * The batch's size on input to the process method. - * @param scratch1 - * Pre-allocated storage to internal use. - * @param scratch2 - * Pre-allocated storage to internal use. - */ - public void finishOuterRepeated(VectorizedRowBatch batch, JoinUtil.JoinResult joinResult, - VectorMapJoinHashMapResult hashMapResult, boolean someRowsFilteredOut, - boolean inputSelectedInUse, int inputLogicalSize) - throws IOException, HiveException { - - // LOG.debug("finishOuterRepeated batch #" + batchCounter + " " + joinResult.name() + " batch.size " + batch.size + " someRowsFilteredOut " + someRowsFilteredOut); - - switch (joinResult) { - case MATCH: - - // Rows we looked up as one repeated key are a match. But filtered out rows - // need to be generated as non-matches, too. - - if (someRowsFilteredOut) { - - // For the filtered out rows that didn't (logically) get looked up in the hash table, - // we need to generate no match results for those too... - - // Run value expressions over original (whole) input batch. - doValueExprOnInputSelected(batch, inputSelectedInUse, inputLogicalSize); - - // Now calculate which rows were filtered out (they are logically no matches). - - // Determine which rows are non matches by determining the delta between inputSelected and - // (current) batch selected. - - int noMatchCount = subtractFromInputSelected( - inputSelectedInUse, inputLogicalSize, batch.selected, batch.size, noMatchs); - - generateOuterNulls(batch, noMatchs, noMatchCount); - - // Now generate the matchs. Single small table values will be put into the big table - // batch and come back in matchs. Any multiple small table value results will go into - // the overflow batch. - generateHashMapResultRepeatedAll(batch, hashMapResult); - - // Merge noMatchs and (match) selected. - int mergeCount = sortMerge( - noMatchs, noMatchCount, batch.selected, batch.size, merged); - - System.arraycopy(merged, 0, batch.selected, 0, mergeCount); - batch.size = mergeCount; - batch.selectedInUse = true; - } else { - - // Just run our value expressions over input batch. - - if (bigTableValueExpressions != null) { - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } - } - - generateHashMapResultRepeatedAll(batch, hashMapResult); - } - break; - - case SPILL: - - // Rows we looked up as one repeated key need to spill. But filtered out rows - // need to be generated as non-matches, too. - - spillBatchRepeated(batch, (VectorMapJoinHashTableResult) hashMapResult); - - // After using selected to generate spills, generate non-matches, if any. - if (someRowsFilteredOut) { - - // Determine which rows are non matches by determining the delta between inputSelected and - // (current) batch selected. - - int noMatchCount = subtractFromInputSelected( - inputSelectedInUse, inputLogicalSize, batch.selected, batch.size, noMatchs); - - System.arraycopy(noMatchs, 0, batch.selected, 0, noMatchCount); - batch.size = noMatchCount; - batch.selectedInUse = true; - - generateOuterNullsRepeatedAll(batch); - } else { - batch.size = 0; - } - - break; - - case NOMATCH: - - if (someRowsFilteredOut) { - - // When the repeated no match is due to filtering, we need to restore the - // selected information. - - if (inputSelectedInUse) { - System.arraycopy(inputSelected, 0, batch.selected, 0, inputLogicalSize); - } - batch.size = inputLogicalSize; - } - - // Run our value expressions over whole batch. - if (bigTableValueExpressions != null) { - for(VectorExpression ve: bigTableValueExpressions) { - ve.evaluate(batch); - } - } - - generateOuterNullsRepeatedAll(batch); - break; - } - } - - /** - * Generate the non-match outer join output results for the whole repeating vectorized - * row batch. - * - * Each row will get nulls for all small table values. - * - * @param batch - * The big table batch. - */ - protected void generateOuterNullsRepeatedAll(VectorizedRowBatch batch) throws HiveException { - - for (int column : smallTableOutputVectorColumns) { - ColumnVector colVector = batch.cols[column]; - colVector.noNulls = false; - colVector.isNull[0] = true; - colVector.isRepeating = true; - } - - // Mark any scratch small table scratch columns that would normally receive a copy of the key - // as null, too. - for (int column : bigTableOuterKeyOutputVectorColumns) { - ColumnVector colVector = batch.cols[column]; - colVector.noNulls = false; - colVector.isNull[0] = true; - colVector.isRepeating = true; - } - } + @Override + public void closeOp(boolean aborted) throws HiveException { + super.closeOp(aborted); + if (!aborted) { + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " closeOp outer join " + + outerJoinNullKeyCounter + " null keys, " + + outerJoinFilteredOutCounter + " filtered out rows"); + } + } + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterLongOperator.java index 5b687fd..6c9158c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterLongOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterLongOperator.java @@ -23,8 +23,9 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; @@ -33,10 +34,8 @@ import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column Long hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMap; - -// Single-Column Long specific imports. -import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; /* * Specialized class for doing a vectorized map join that is an outer join on a Single-Column Long @@ -44,8 +43,17 @@ */ public class VectorMapJoinOuterLongOperator extends VectorMapJoinOuterGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOuterLongOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinOuterLongOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -54,7 +62,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinLongHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Single-Column Long specific members. @@ -67,6 +75,10 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + private transient PrimitiveTypeInfo singleJoinColumnPrimitiveTypeInfo; + + // The object that determines equal key series. + private transient VectorKeySeriesLong longKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -101,6 +113,10 @@ public void process(Object row, int tag) throws HiveException { */ singleJoinColumn = bigTableKeyColumnMap[0]; + singleJoinColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) bigTableKeyTypeInfos[0]; + + longKeySeries = + new VectorKeySeriesLong(singleJoinColumn, singleJoinColumnPrimitiveTypeInfo); needCommonSetup = false; } @@ -113,7 +129,7 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column Long hash map information for this specialized class. */ - hashMap = (VectorMapJoinLongHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; useMinMax = hashMap.useMinMax(); if (useMinMax) { min = hashMap.min(); @@ -124,313 +140,129 @@ public void process(Object row, int tag) throws HiveException { } batchCounter++; + inputRowCounter += batch.size; - final int inputLogicalSize = batch.size; + // Do the per-batch setup for an outer join. - if (inputLogicalSize == 0) { + if (!outerPerBatchSetup(batch)) { if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + LOG.info(getLoggingPrefix() + " batch #" + batchCounter + " empty"); } return; } - // Do the per-batch setup for an outer join. - - outerPerBatchSetup(batch); - - // For outer join, remember our input rows before ON expression filtering or before - // hash table matching so we can generate results for all rows (matching and non matching) - // later. - boolean inputSelectedInUse = batch.selectedInUse; - if (inputSelectedInUse) { - // if (!verifyMonotonicallyIncreasing(batch.selected, batch.size)) { - // throw new HiveException("batch.selected is not in sort order and unique"); - // } - System.arraycopy(batch.selected, 0, inputSelected, 0, inputLogicalSize); - } - - // Filtering for outer join just removes rows available for hash table matching. - boolean someRowsFilteredOut = false; - if (bigTableFilterExpressions.length > 0) { - // Since the input - for (VectorExpression ve : bigTableFilterExpressions) { - ve.evaluate(batch); - } - someRowsFilteredOut = (batch.size != inputLogicalSize); - if (isLogDebugEnabled) { - if (batch.selectedInUse) { - if (inputSelectedInUse) { - LOG.debug(CLASS_NAME + - " inputSelected " + intArrayToRangesString(inputSelected, inputLogicalSize) + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } else { - LOG.debug(CLASS_NAME + - " inputLogicalSize " + inputLogicalSize + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } - } - } - } + if (filteredSize > 0) { + longKeySeries.processBatch(batch); + // LOG.info(getLoggingPrefix() + " seriesCount " + longKeySeries.getSeriesCount() + " nonNullKeyCount " + + // longKeySeries.getNonNullKeyCount()); - // Perform any key expressions. Results will go into scratch columns. - if (bigTableKeyExpressions != null) { - for (VectorExpression ve : bigTableKeyExpressions) { - ve.evaluate(batch); - } - } - /* - * Single-Column Long specific declarations. - */ + int matchSeriesCount = 0; + int spillSeriesCount = 0; + int hashMapResultCount = 0; - // The one join column for this specialized class. - LongColumnVector joinColVector = (LongColumnVector) batch.cols[singleJoinColumn]; - long[] vector = joinColVector.vector; + // UNDONE: Debugging + int logical; + int batchIndex; + boolean selectedInUse = batch.selectedInUse; + int[] selected = batch.selected; - /* - * Single-Column Long check for repeating. - */ + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + long key; + do { + logical = longKeySeries.getCurrentLogical(); + batchIndex = (selectedInUse ? selected[logical] : logical); - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; + // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, getLoggingPrefix() + " candidate batch"); - if (allKeyInputColumnsRepeating) { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - /* - * Repeating. - */ + if (longKeySeries.getCurrentIsAllNull()) { - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. + // CONSIDER: Add support for NullSafe option. - /* - * Single-Column Long specific repeated lookup. - */ + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; + outerJoinNullKeyCounter += longKeySeries.getCurrentDuplicateCount(); - JoinUtil.JoinResult joinResult; - if (batch.size == 0) { - // Whole repeated key batch was filtered out. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else if (!joinColVector.noNulls && joinColVector.isNull[0]) { - // Any (repeated) null key column is no match for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - // Handle *repeated* join key, if found. - long key = vector[0]; - // LOG.debug(CLASS_NAME + " repeated key " + key); - if (useMinMax && (key < min || key > max)) { - // Out of range for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; + // LOG.info(getLoggingPrefix() + " logical " + logical + " batchIndex " + batchIndex + " NULL"); } else { - joinResult = hashMap.lookup(key, hashMapResults[0]); - } - } - - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishOuterRepeated(batch, joinResult, hashMapResults[0], someRowsFilteredOut, - inputSelectedInUse, inputLogicalSize); - } else { - - /* - * NOT Repeating. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); - } - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; - - int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; + key = longKeySeries.getCurrentKey(); - boolean atLeastOneNonMatch = someRowsFilteredOut; - - /* - * Single-Column Long specific variables. - */ - - long saveKey = 0; - - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; + if (useMinMax && (key < min || key > max)) { + // Out of range for whole batch. + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; + } else { + hashMap.hashMapLookup( + key, + longKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); + } - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < batch.size; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); + // LOG.info(getLoggingPrefix() + " key " + key + + // " hashCode " + Integer.toHexString(longKeySeries.getCurrentHashCode()) + + // " lookupResult " + lookupResult.name()); - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, taskName + ", " + getOperatorId() + " candidate " + CLASS_NAME + " batch"); + } /* - * Single-Column Long outer null detection. + * Common inner join result processing. */ - boolean isNull = !joinColVector.noNulls && joinColVector.isNull[batchIndex]; - - if (isNull) { - - // Have that the NULL does not interfere with the current equal key series, if there - // is one. We do not set saveJoinResult. - // - // Let a current MATCH equal key series keep going, or - // Let a current SPILL equal key series keep going, or - // Let a current NOMATCH keep not matching. - - atLeastOneNonMatch = true; - - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " NULL"); - } else { - - /* - * Single-Column Long outer get key. - */ - - long currentKey = vector[batchIndex]; - - /* - * Equal key series checking. - */ - - if (!haveSaveKey || currentKey != saveKey) { - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column Long specific save key. - */ - - saveKey = currentKey; - - /* - * Single-Column Long specific lookup key. - */ - - if (useMinMax && (currentKey < min || currentKey > max)) { - // Key out of range for whole hash table. - saveJoinResult = JoinUtil.JoinResult.NOMATCH; - } else { - saveJoinResult = hashMap.lookup(currentKey, hashMapResults[hashMapResultCount]); - } - - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " New Key " + currentKey + " " + saveJoinResult.name()); - - /* - * Common outer join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - atLeastOneNonMatch = true; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " Key Continues " + saveKey + " " + saveJoinResult.name()); - - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - // if (!verifyMonotonicallyIncreasing(allMatchs, allMatchCount)) { - // throw new HiveException("allMatchs is not in sort order and unique"); - // } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { + switch (lookupResult) { case MATCH: + matchLogicalIndices[matchSeriesCount] = longKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; hashMapResultCount++; - equalKeySeriesCount++; + // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, getLoggingPrefix() + " MATCH"); break; + case SPILL: + spillLogicalIndices[spillSeriesCount] = longKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = longKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; hashMapResultCount++; + spilledRowCounter += longKeySeries.getCurrentDuplicateCount(); break; - case NOMATCH: + + case NO_MATCH: + // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, getLoggingPrefix() + " NOMATCH"); break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - } + + if (!longKeySeries.next()) { + break; + } + } while (true); if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " atLeastOneNonMatch " + atLeastOneNonMatch + - " inputSelectedInUse " + inputSelectedInUse + + LOG.debug(getLoggingPrefix() + " batch #" + batchCounter + " batch info " + " inputLogicalSize " + inputLogicalSize + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " matchHashMapResults " + Arrays.toString(Arrays.copyOfRange(matchHashMapResults, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } - - // We will generate results for all matching and non-matching rows. - finishOuter(batch, - allMatchCount, equalKeySeriesCount, atLeastOneNonMatch, - inputSelectedInUse, inputLogicalSize, - spillCount, hashMapResultCount); + finishOuter(batch, matchSeriesCount, spillSeriesCount); + } else { + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " batch #" + batchCounter + " batch info " + + " inputLogicalSize " + inputLogicalSize + " filtered out"); + } + finishOuter(batch, 0, 0); } if (batch.size > 0) { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterMultiKeyOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterMultiKeyOperator.java index e212a2a..3febc47 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterMultiKeyOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterMultiKeyOperator.java @@ -23,20 +23,15 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesMultiFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; - -// Multi-Key hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; - -// Multi-Key specific imports. -import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; -import org.apache.hadoop.hive.serde2.ByteStream.Output; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; /* @@ -46,8 +41,17 @@ public class VectorMapJoinOuterMultiKeyOperator extends VectorMapJoinOuterGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOuterMultiKeyOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinOuterMultiKeyOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -56,20 +60,17 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Multi-Key specific members. // - // Object that can take a set of columns in row in a vectorized row batch and serialized it. - private transient VectorSerializeRow keyVectorSerializeWrite; - - // The BinarySortable serialization of the current key. - private transient Output currentKeyOutput; + // Binary sortable multi-key serializer. + private transient BinarySortableSerializeWrite binarySortableSerializeWrite; - // The BinarySortable serialization of the saved key for a possible series of equal keys. - private transient Output saveKeyOutput; + // The object that determines equal key series. + private transient VectorKeySeriesMultiFast serializedMultiKeySeries; //--------------------------------------------------------------------------- // Pass-thru constructors. @@ -103,12 +104,15 @@ public void process(Object row, int tag) throws HiveException { * Initialize Multi-Key members for this specialized class. */ - keyVectorSerializeWrite = new VectorSerializeRow( - new BinarySortableSerializeWrite(bigTableKeyColumnMap.length)); - keyVectorSerializeWrite.init(bigTableKeyTypeNames, bigTableKeyColumnMap); + binarySortableSerializeWrite = + new BinarySortableSerializeWrite(bigTableKeyColumnMap.length); - currentKeyOutput = new Output(); - saveKeyOutput = new Output(); + // For Multi-Key the Fast hash table computes the serialized key's hash code, + // so we don't use VectorKeySeriesMulti's hash code. + serializedMultiKeySeries = + new VectorKeySeriesMultiFast( + binarySortableSerializeWrite); + serializedMultiKeySeries.init(bigTableKeyTypeInfos, bigTableKeyColumnMap); needCommonSetup = false; } @@ -121,332 +125,113 @@ public void process(Object row, int tag) throws HiveException { * Get our Multi-Key hash map information for this specialized class. */ - hashMap = (VectorMapJoinBytesHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; - final int inputLogicalSize = batch.size; + // Do the per-batch setup for an outer join. - if (inputLogicalSize == 0) { + if (!outerPerBatchSetup(batch)) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } return; } - // Do the per-batch setup for an outer join. - - outerPerBatchSetup(batch); - - // For outer join, remember our input rows before ON expression filtering or before - // hash table matching so we can generate results for all rows (matching and non matching) - // later. - boolean inputSelectedInUse = batch.selectedInUse; - if (inputSelectedInUse) { - // if (!verifyMonotonicallyIncreasing(batch.selected, batch.size)) { - // throw new HiveException("batch.selected is not in sort order and unique"); - // } - System.arraycopy(batch.selected, 0, inputSelected, 0, inputLogicalSize); - } - - // Filtering for outer join just removes rows available for hash table matching. - boolean someRowsFilteredOut = false; - if (bigTableFilterExpressions.length > 0) { - // Since the input - for (VectorExpression ve : bigTableFilterExpressions) { - ve.evaluate(batch); - } - someRowsFilteredOut = (batch.size != inputLogicalSize); - if (isLogDebugEnabled) { - if (batch.selectedInUse) { - if (inputSelectedInUse) { - LOG.debug(CLASS_NAME + - " inputSelected " + intArrayToRangesString(inputSelected, inputLogicalSize) + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } else { - LOG.debug(CLASS_NAME + - " inputLogicalSize " + inputLogicalSize + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } - } - } - } - - // Perform any key expressions. Results will go into scratch columns. - if (bigTableKeyExpressions != null) { - for (VectorExpression ve : bigTableKeyExpressions) { - ve.evaluate(batch); - } - } - - /* - * Multi-Key specific declarations. - */ - - // None. - - /* - * Multi-Key Long check for repeating. - */ - - // If all BigTable input columns to key expressions are isRepeating, then - // calculate key once; lookup once. - // Also determine if any nulls are present since for a join that means no match. - boolean allKeyInputColumnsRepeating; - boolean someKeyInputColumnIsNull = false; // Only valid if allKeyInputColumnsRepeating is true. - if (bigTableKeyColumnMap.length == 0) { - allKeyInputColumnsRepeating = false; - } else { - allKeyInputColumnsRepeating = true; - for (int i = 0; i < bigTableKeyColumnMap.length; i++) { - ColumnVector colVector = batch.cols[bigTableKeyColumnMap[i]]; - if (!colVector.isRepeating) { - allKeyInputColumnsRepeating = false; - break; - } - if (!colVector.noNulls && colVector.isNull[0]) { - someKeyInputColumnIsNull = true; - } - } - } - - if (allKeyInputColumnsRepeating) { - - /* - * Repeating. - */ - - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. - - /* - * Multi-Key specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (batch.size == 0) { - // Whole repeated key batch was filtered out. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else if (someKeyInputColumnIsNull) { - // Any (repeated) null key column is no match for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - - // All key input columns are repeating. Generate key once. Lookup once. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, 0); - byte[] keyBytes = currentKeyOutput.getData(); - int keyLength = currentKeyOutput.getLength(); - joinResult = hashMap.lookup(keyBytes, 0, keyLength, hashMapResults[0]); - } - - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishOuterRepeated(batch, joinResult, hashMapResults[0], someRowsFilteredOut, - inputSelectedInUse, inputLogicalSize); - } else { - - /* - * NOT Repeating. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); - } - - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; + if (filteredSize > 0) { + serializedMultiKeySeries.processBatch(batch); + int matchSeriesCount = 0; + int spillSeriesCount = 0; int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - - boolean atLeastOneNonMatch = someRowsFilteredOut; - - /* - * Multi-Key specific variables. - */ - Output temp; + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + do { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; - - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < batch.size; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); - - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, taskName + ", " + getOperatorId() + " candidate " + CLASS_NAME + " batch"); - - /* - * Multi-Key outer null detection. + /** + * NOTE: the usage of ~HasAnyNull because any null key in the multi-column key means + * NO_MATCH for Outer Join. */ + if (serializedMultiKeySeries.getCurrentIsAllNull() || + serializedMultiKeySeries.getCurrentHasAnyNulls()) { - // Generate binary sortable key for current row in vectorized row batch. - keyVectorSerializeWrite.setOutput(currentKeyOutput); - keyVectorSerializeWrite.serializeWrite(batch, batchIndex); - if (keyVectorSerializeWrite.getHasAnyNulls()) { - - // Have that the NULL does not interfere with the current equal key series, if there - // is one. We do not set saveJoinResult. - // - // Let a current MATCH equal key series keep going, or - // Let a current SPILL equal key series keep going, or - // Let a current NOMATCH keep not matching. + // CONSIDER: Add support for NullSafe option. - atLeastOneNonMatch = true; + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; + outerJoinNullKeyCounter += serializedMultiKeySeries.getCurrentDuplicateCount(); - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " NULL"); } else { - /* - * Multi-Key outer get key. - */ - - // Generated earlier to get possible null(s). - - /* - * Equal key series checking. - */ - - if (!haveSaveKey || !saveKeyOutput.arraysEquals(currentKeyOutput)) { - - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Multi-Key specific save key. - */ - - temp = saveKeyOutput; - saveKeyOutput = currentKeyOutput; - currentKeyOutput = temp; - - /* - * Multi-Key specific lookup key. - */ - - byte[] keyBytes = saveKeyOutput.getData(); - int keyLength = saveKeyOutput.getLength(); - saveJoinResult = hashMap.lookup(keyBytes, 0, keyLength, hashMapResults[hashMapResultCount]); - - /* - * Common outer join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - atLeastOneNonMatch = true; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " Key Continues " + saveKey + " " + saveJoinResult.name()); - - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - // if (!verifyMonotonicallyIncreasing(allMatchs, allMatchCount)) { - // throw new HiveException("allMatchs is not in sort order and unique"); - // } + hashMap.hashMapLookup( + serializedMultiKeySeries.getSerializedBytes(), + serializedMultiKeySeries.getSerializedStart(), + serializedMultiKeySeries.getSerializedLength(), + serializedMultiKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); } - } - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { + /* + * Common inner join result processing. + */ + + switch (lookupResult) { case MATCH: + matchLogicalIndices[matchSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; hashMapResultCount++; - equalKeySeriesCount++; break; + case SPILL: + spillLogicalIndices[spillSeriesCount] = serializedMultiKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = serializedMultiKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; hashMapResultCount++; + spilledRowCounter += serializedMultiKeySeries.getCurrentDuplicateCount(); break; - case NOMATCH: + + case NO_MATCH: break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - } + + if (!serializedMultiKeySeries.next()) { + break; + } + } while (true); if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " atLeastOneNonMatch " + atLeastOneNonMatch + - " inputSelectedInUse " + inputSelectedInUse + + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + " inputLogicalSize " + inputLogicalSize + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " matchHashMapResults " + Arrays.toString(Arrays.copyOfRange(matchHashMapResults, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } - // We will generate results for all matching and non-matching rows. - finishOuter(batch, - allMatchCount, equalKeySeriesCount, atLeastOneNonMatch, - inputSelectedInUse, inputLogicalSize, - spillCount, hashMapResultCount); + finishOuter(batch, matchSeriesCount, spillSeriesCount); + } else { + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " batch #" + batchCounter + " batch info " + + " inputLogicalSize " + inputLogicalSize + " filtered out"); + } + finishOuter(batch, 0, 0); } if (batch.size > 0) { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterStringOperator.java index e4107ff..9cd0807 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterStringOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinOuterStringOperator.java @@ -23,7 +23,9 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFind; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; @@ -31,11 +33,7 @@ import org.apache.hadoop.hive.ql.plan.OperatorDesc; // Single-Column String hash table import. -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; - -// Single-Column String specific imports. -import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector; -import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytes; /* * Specialized class for doing a vectorized map join that is an outer join on a Single-Column String @@ -44,8 +42,17 @@ public class VectorMapJoinOuterStringOperator extends VectorMapJoinOuterGenerateResultOperator { private static final long serialVersionUID = 1L; - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOuterStringOperator.class.getName()); + + //------------------------------------------------------------------------------------------------ + private static final String CLASS_NAME = VectorMapJoinOuterStringOperator.class.getName(); + private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME); + + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + + //------------------------------------------------------------------------------------------------ // (none) @@ -54,7 +61,7 @@ //--------------------------------------------------------------------------- // The hash map for this specialized class. - private transient VectorMapJoinBytesHashMap hashMap; + private transient MapJoinHashTableFind hashMap; //--------------------------------------------------------------------------- // Single-Column String specific members. @@ -63,6 +70,9 @@ // The column number for this one column join specialization. private transient int singleJoinColumn; + // The object that determines equal key series. + private transient VectorKeySeriesBytes bytesKeySeries; + //--------------------------------------------------------------------------- // Pass-thru constructors. // @@ -97,6 +107,8 @@ public void process(Object row, int tag) throws HiveException { singleJoinColumn = bigTableKeyColumnMap[0]; + bytesKeySeries = new VectorKeySeriesBytes(singleJoinColumn); + needCommonSetup = false; } @@ -108,315 +120,109 @@ public void process(Object row, int tag) throws HiveException { * Get our Single-Column String hash map information for this specialized class. */ - hashMap = (VectorMapJoinBytesHashMap) vectorMapJoinHashTable; + hashMap = vectorMapJoinHashTableFind; needHashTableSetup = false; } batchCounter++; + inputRowCounter += batch.size; - final int inputLogicalSize = batch.size; + // Do the per-batch setup for an outer join. - if (inputLogicalSize == 0) { + if (!outerPerBatchSetup(batch)) { if (isLogDebugEnabled) { LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); } return; } - // Do the per-batch setup for an outer join. - - outerPerBatchSetup(batch); - - // For outer join, remember our input rows before ON expression filtering or before - // hash table matching so we can generate results for all rows (matching and non matching) - // later. - boolean inputSelectedInUse = batch.selectedInUse; - if (inputSelectedInUse) { - // if (!verifyMonotonicallyIncreasing(batch.selected, batch.size)) { - // throw new HiveException("batch.selected is not in sort order and unique"); - // } - System.arraycopy(batch.selected, 0, inputSelected, 0, inputLogicalSize); - } - - // Filtering for outer join just removes rows available for hash table matching. - boolean someRowsFilteredOut = false; - if (bigTableFilterExpressions.length > 0) { - // Since the input - for (VectorExpression ve : bigTableFilterExpressions) { - ve.evaluate(batch); - } - someRowsFilteredOut = (batch.size != inputLogicalSize); - if (isLogDebugEnabled) { - if (batch.selectedInUse) { - if (inputSelectedInUse) { - LOG.debug(CLASS_NAME + - " inputSelected " + intArrayToRangesString(inputSelected, inputLogicalSize) + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } else { - LOG.debug(CLASS_NAME + - " inputLogicalSize " + inputLogicalSize + - " filtered batch.selected " + intArrayToRangesString(batch.selected, batch.size)); - } - } - } - } - - // Perform any key expressions. Results will go into scratch columns. - if (bigTableKeyExpressions != null) { - for (VectorExpression ve : bigTableKeyExpressions) { - ve.evaluate(batch); - } - } - - /* - * Single-Column String specific declarations. - */ - - // The one join column for this specialized class. - BytesColumnVector joinColVector = (BytesColumnVector) batch.cols[singleJoinColumn]; - byte[][] vector = joinColVector.vector; - int[] start = joinColVector.start; - int[] length = joinColVector.length; - - /* - * Single-Column String check for repeating. - */ - - // Check single column for repeating. - boolean allKeyInputColumnsRepeating = joinColVector.isRepeating; - - if (allKeyInputColumnsRepeating) { - - /* - * Repeating. - */ - - // All key input columns are repeating. Generate key once. Lookup once. - // Since the key is repeated, we must use entry 0 regardless of selectedInUse. - - /* - * Single-Column String specific repeated lookup. - */ - - JoinUtil.JoinResult joinResult; - if (batch.size == 0) { - // Whole repeated key batch was filtered out. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else if (!joinColVector.noNulls && joinColVector.isNull[0]) { - // Any (repeated) null key column is no match for whole batch. - joinResult = JoinUtil.JoinResult.NOMATCH; - } else { - // Handle *repeated* join key, if found. - byte[] keyBytes = vector[0]; - int keyStart = start[0]; - int keyLength = length[0]; - joinResult = hashMap.lookup(keyBytes, keyStart, keyLength, hashMapResults[0]); - } - - /* - * Common repeated join result processing. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name()); - } - finishOuterRepeated(batch, joinResult, hashMapResults[0], someRowsFilteredOut, - inputSelectedInUse, inputLogicalSize); - } else { - - /* - * NOT Repeating. - */ - - if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated"); - } - - int selected[] = batch.selected; - boolean selectedInUse = batch.selectedInUse; + if (filteredSize > 0) { + bytesKeySeries.processBatch(batch); + int matchSeriesCount = 0; + int spillSeriesCount = 0; int hashMapResultCount = 0; - int allMatchCount = 0; - int equalKeySeriesCount = 0; - int spillCount = 0; - boolean atLeastOneNonMatch = someRowsFilteredOut; + MapJoinHashMapResult hashMapResult; + MapJoinHashTableResult.MapJoinResult lookupResult; + do { + // Use the next hash map result entry. + hashMapResult = hashMapResults[hashMapResultCount]; - /* - * Single-Column String specific variables. - */ + if (bytesKeySeries.getCurrentIsAllNull()) { - int saveKeyBatchIndex = -1; + // CONSIDER: Add support for NullSafe option. - // We optimize performance by only looking up the first key in a series of equal keys. - boolean haveSaveKey = false; - JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH; + lookupResult = MapJoinHashTableResult.MapJoinResult.NO_MATCH; + outerJoinNullKeyCounter += bytesKeySeries.getCurrentDuplicateCount(); - // Logical loop over the rows in the batch since the batch may have selected in use. - for (int logical = 0; logical < batch.size; logical++) { - int batchIndex = (selectedInUse ? selected[logical] : logical); + } else { + + hashMap.hashMapLookup( + bytesKeySeries.getCurrentBytes(), + bytesKeySeries.getCurrentStart(), + bytesKeySeries.getCurrentLength(), + bytesKeySeries.getCurrentHashCode(), + hashMapResult); + lookupResult = hashMapResult.getMapJoinResult(); - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, taskName + ", " + getOperatorId() + " candidate " + CLASS_NAME + " batch"); + } /* - * Single-Column String outer null detection. + * Common inner join result processing. */ - boolean isNull = !joinColVector.noNulls && joinColVector.isNull[batchIndex]; - - if (isNull) { - - // Have that the NULL does not interfere with the current equal key series, if there - // is one. We do not set saveJoinResult. - // - // Let a current MATCH equal key series keep going, or - // Let a current SPILL equal key series keep going, or - // Let a current NOMATCH keep not matching. - - atLeastOneNonMatch = true; - - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " NULL"); - } else { - - /* - * Single-Column String outer get key. - */ - - // Implicit -- use batchIndex. - - /* - * Equal key series checking. - */ - - if (!haveSaveKey || - StringExpr.equal(vector[saveKeyBatchIndex], start[saveKeyBatchIndex], length[saveKeyBatchIndex], - vector[batchIndex], start[batchIndex], length[batchIndex]) == false) { - // New key. - - if (haveSaveKey) { - // Move on with our counts. - switch (saveJoinResult) { - case MATCH: - hashMapResultCount++; - equalKeySeriesCount++; - break; - case SPILL: - hashMapResultCount++; - break; - case NOMATCH: - break; - } - } - - // Regardless of our matching result, we keep that information to make multiple use - // of it for a possible series of equal keys. - haveSaveKey = true; - - /* - * Single-Column String specific save key. - */ - - saveKeyBatchIndex = batchIndex; - - /* - * Single-Column Long specific lookup key. - */ - - byte[] keyBytes = vector[batchIndex]; - int keyStart = start[batchIndex]; - int keyLength = length[batchIndex]; - saveJoinResult = hashMap.lookup(keyBytes, keyStart, keyLength, hashMapResults[hashMapResultCount]); - - /* - * Common outer join result processing. - */ - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesHashMapResultIndices[equalKeySeriesCount] = hashMapResultCount; - equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount; - equalKeySeriesIsSingleValue[equalKeySeriesCount] = hashMapResults[hashMapResultCount].isSingleRow(); - equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - atLeastOneNonMatch = true; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey); - break; - } - } else { - // LOG.debug(CLASS_NAME + " logical " + logical + " batchIndex " + batchIndex + " Key Continues " + saveKey + " " + saveJoinResult.name()); - - // Series of equal keys. - - switch (saveJoinResult) { - case MATCH: - equalKeySeriesDuplicateCounts[equalKeySeriesCount]++; - allMatchs[allMatchCount++] = batchIndex; - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate"); - break; - - case SPILL: - spills[spillCount] = batchIndex; - spillHashMapResultIndices[spillCount] = hashMapResultCount; - spillCount++; - break; - - case NOMATCH: - // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate"); - break; - } - } - // if (!verifyMonotonicallyIncreasing(allMatchs, allMatchCount)) { - // throw new HiveException("allMatchs is not in sort order and unique"); - // } - } - } - - if (haveSaveKey) { - // Update our counts for the last key. - switch (saveJoinResult) { + switch (lookupResult) { case MATCH: + matchLogicalIndices[matchSeriesCount] = bytesKeySeries.getCurrentLogical(); + matchDuplicateCounts[matchSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + matchIsSingleValue[matchSeriesCount] = hashMapResult.isSingleRow(); + matchHashMapResults[matchSeriesCount] = hashMapResult; + matchSeriesCount++; hashMapResultCount++; - equalKeySeriesCount++; break; + case SPILL: + spillLogicalIndices[spillSeriesCount] = bytesKeySeries.getCurrentLogical(); + spillDuplicateCounts[spillSeriesCount] = bytesKeySeries.getCurrentDuplicateCount(); + spillHashMapResults[spillSeriesCount] = hashMapResult; + spillSeriesCount++; hashMapResultCount++; + spilledRowCounter += bytesKeySeries.getCurrentDuplicateCount(); break; - case NOMATCH: + + case NO_MATCH: break; + + default: + throw new RuntimeException("Unexpected lookup result " + lookupResult.name()); } - } + + if (!bytesKeySeries.next()) { + break; + } + } while (true); if (isLogDebugEnabled) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + - " allMatchs " + intArrayToRangesString(allMatchs,allMatchCount) + - " equalKeySeriesHashMapResultIndices " + intArrayToRangesString(equalKeySeriesHashMapResultIndices, equalKeySeriesCount) + - " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + - " equalKeySeriesIsSingleValue " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesIsSingleValue, 0, equalKeySeriesCount)) + - " equalKeySeriesDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(equalKeySeriesDuplicateCounts, 0, equalKeySeriesCount)) + - " atLeastOneNonMatch " + atLeastOneNonMatch + - " inputSelectedInUse " + inputSelectedInUse + + LOG.debug(CLASS_NAME + " batch #" + batchCounter + " batch info " + " inputLogicalSize " + inputLogicalSize + - " spills " + intArrayToRangesString(spills, spillCount) + - " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + - " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMapResults, 0, hashMapResultCount))); + " matchSeriesCount " + matchSeriesCount + + " matchLogicalIndices " + intArrayToRangesString(matchLogicalIndices, matchSeriesCount) + + " matchDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(matchDuplicateCounts, 0, matchSeriesCount)) + + " matchHashMapResults " + Arrays.toString(Arrays.copyOfRange(matchHashMapResults, 0, matchSeriesCount)) + + " spillSeriesCount " + spillSeriesCount + + " spillLogicalIndices " + intArrayToRangesString(spillLogicalIndices, spillSeriesCount) + + " spillDuplicateCounts " + Arrays.toString(Arrays.copyOfRange(spillDuplicateCounts, 0, spillSeriesCount))); } - // We will generate results for all matching and non-matching rows. - finishOuter(batch, - allMatchCount, equalKeySeriesCount, atLeastOneNonMatch, - inputSelectedInUse, inputLogicalSize, - spillCount, hashMapResultCount); + finishOuter(batch, matchSeriesCount, spillSeriesCount); + } else { + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " batch #" + batchCounter + " batch info " + + " inputLogicalSize " + inputLogicalSize + " filtered out"); + } + finishOuter(batch, 0, 0); } if (batch.size > 0) { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMap.java index 0ff98bd..dd7dafc 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMap.java @@ -20,31 +20,24 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single byte array value hash map optimized for vector map join. */ -public abstract class VectorMapJoinFastBytesHashMap - extends VectorMapJoinFastBytesHashTable - implements VectorMapJoinBytesHashMap { +public abstract class VectorMapJoinFastBytesHashMap extends VectorMapJoinFastBytesHashTable { private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastBytesHashMap.class); private VectorMapJoinFastValueStore valueStore; @Override - public VectorMapJoinHashMapResult createHashMapResult() { - return new VectorMapJoinFastValueStore.HashMapResult(); - } - - @Override public void assignSlot(int slot, byte[] keyBytes, int keyStart, int keyLength, - long hashCode, boolean isNewKey, BytesWritable currentValue) { + long longHashCode, boolean isNewKey, BytesWritable currentValue) { + + // LOG.info("VectorMapJoinFastBytesHashMap assignSlot " + + // VectorizedBatchUtil.displayBytes(keyBytes, keyStart, keyLength) + " hashCode " + Long.toHexString(longHashCode)); byte[] valueBytes = currentValue.getBytes(); int valueLength = currentValue.getLength(); @@ -53,46 +46,37 @@ public void assignSlot(int slot, byte[] keyBytes, int keyStart, int keyLength, if (isNewKey) { // First entry. slotTriples[tripleIndex] = keyStore.add(keyBytes, keyStart, keyLength); - slotTriples[tripleIndex + 1] = hashCode; + slotTriples[tripleIndex + 1] = longHashCode; slotTriples[tripleIndex + 2] = valueStore.addFirst(valueBytes, 0, valueLength); - // LOG.debug("VectorMapJoinFastBytesHashMap add first keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); keysAssigned++; } else { // Add another value. - // LOG.debug("VectorMapJoinFastBytesHashMap add more keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); slotTriples[tripleIndex + 2] = valueStore.addMore(slotTriples[tripleIndex + 2], valueBytes, 0, valueLength); - // LOG.debug("VectorMapJoinFastBytesHashMap add more new valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); } + numValues++; } @Override - public JoinUtil.JoinResult lookup(byte[] keyBytes, int keyStart, int keyLength, VectorMapJoinHashMapResult hashMapResult) { - VectorMapJoinFastValueStore.HashMapResult optimizedHashMapResult = + public void hashMapLookup(byte[] keyBytes, int keyStart, int keyLength, + int hashCode, MapJoinHashMapResult hashMapResult) { + + VectorMapJoinFastValueStore.HashMapResult fastHashMapResult = (VectorMapJoinFastValueStore.HashMapResult) hashMapResult; - optimizedHashMapResult.forget(); + fastHashMapResult.forget(); - long hashCode = HashCodeUtil.murmurHash(keyBytes, keyStart, keyLength); long valueRefWord = findReadSlot(keyBytes, keyStart, keyLength, hashCode); - JoinUtil.JoinResult joinResult; if (valueRefWord == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + hashMapResult.setNoMatch(); } else { - // LOG.debug("VectorMapJoinFastBytesHashMap lookup hashCode " + Long.toHexString(hashCode) + " valueRefWord " + Long.toHexString(valueRefWord) + " (valueStore != null) " + (valueStore != null)); - - optimizedHashMapResult.set(valueStore, valueRefWord); - - joinResult = JoinUtil.JoinResult.MATCH; + fastHashMapResult.setMatch(valueStore, valueRefWord); } - - optimizedHashMapResult.setJoinResult(joinResult); - - return joinResult; } public VectorMapJoinFastBytesHashMap( - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); valueStore = new VectorMapJoinFastValueStore(writeBuffersSize); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMultiSet.java index 5d8ed2d..1b19d54 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMultiSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashMultiSet.java @@ -20,74 +20,51 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single byte array value hash multi-set optimized for vector map join. */ -public abstract class VectorMapJoinFastBytesHashMultiSet - extends VectorMapJoinFastBytesHashTable - implements VectorMapJoinBytesHashMultiSet { +public abstract class VectorMapJoinFastBytesHashMultiSet extends VectorMapJoinFastBytesHashTable { private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastBytesHashMultiSet.class); @Override - public VectorMapJoinHashMultiSetResult createHashMultiSetResult() { - return new VectorMapJoinFastHashMultiSet.HashMultiSetResult(); - } - - @Override public void assignSlot(int slot, byte[] keyBytes, int keyStart, int keyLength, - long hashCode, boolean isNewKey, BytesWritable currentValue) { + long longHashCode, boolean isNewKey, BytesWritable currentValue) { int tripleIndex = 3 * slot; if (isNewKey) { // First entry. slotTriples[tripleIndex] = keyStore.add(keyBytes, keyStart, keyLength); - slotTriples[tripleIndex + 1] = hashCode; + slotTriples[tripleIndex + 1] = longHashCode; slotTriples[tripleIndex + 2] = 1; // Count. - // LOG.debug("VectorMapJoinFastBytesHashMap add first keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); keysAssigned++; } else { // Add another value. - // LOG.debug("VectorMapJoinFastBytesHashMap add more keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); slotTriples[tripleIndex + 2]++; } } @Override - public JoinUtil.JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashMultiSetResult hashMultiSetResult) { - - VectorMapJoinFastHashMultiSet.HashMultiSetResult optimizedHashMultiSetResult = - (VectorMapJoinFastHashMultiSet.HashMultiSetResult) hashMultiSetResult; - - optimizedHashMultiSetResult.forget(); + public void hashMultiSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) { - long hashCode = HashCodeUtil.murmurHash(keyBytes, keyStart, keyLength); + hashMultiSetResult.forget(); long count = findReadSlot(keyBytes, keyStart, keyLength, hashCode); - JoinUtil.JoinResult joinResult; if (count == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + hashMultiSetResult.setNoMatch(); } else { - - optimizedHashMultiSetResult.set(count); - - joinResult = JoinUtil.JoinResult.MATCH; + hashMultiSetResult.setMatch(count); } - - optimizedHashMultiSetResult.setJoinResult(joinResult); - - return joinResult; } public VectorMapJoinFastBytesHashMultiSet( - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); keyStore = new VectorMapJoinFastKeyStore(writeBuffersSize); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashSet.java index 990a2e5..ac74a37 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashSet.java @@ -18,68 +18,50 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; + import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single byte array value hash multi-set optimized for vector map join. */ -public abstract class VectorMapJoinFastBytesHashSet - extends VectorMapJoinFastBytesHashTable - implements VectorMapJoinBytesHashSet { +public abstract class VectorMapJoinFastBytesHashSet extends VectorMapJoinFastBytesHashTable { private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastBytesHashSet.class); @Override - public VectorMapJoinHashSetResult createHashSetResult() { - return new VectorMapJoinFastHashSet.HashSetResult(); - } - - @Override public void assignSlot(int slot, byte[] keyBytes, int keyStart, int keyLength, - long hashCode, boolean isNewKey, BytesWritable currentValue) { + long longHashCode, boolean isNewKey, BytesWritable currentValue) { int tripleIndex = 3 * slot; if (isNewKey) { // First entry. slotTriples[tripleIndex] = keyStore.add(keyBytes, keyStart, keyLength); - slotTriples[tripleIndex + 1] = hashCode; + slotTriples[tripleIndex + 1] = longHashCode; slotTriples[tripleIndex + 2] = 1; // Existence keysAssigned++; } } @Override - public JoinUtil.JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashSetResult hashSetResult) { + public void hashSetContains(byte[] keyBytes, int keyStart, int hashCode, + int keyLength, MapJoinHashSetResult hashSetResult) { - VectorMapJoinFastHashSet.HashSetResult optimizedHashSetResult = - (VectorMapJoinFastHashSet.HashSetResult) hashSetResult; - - optimizedHashSetResult.forget(); - - long hashCode = HashCodeUtil.murmurHash(keyBytes, keyStart, keyLength); + hashSetResult.forget(); long existance = findReadSlot(keyBytes, keyStart, keyLength, hashCode); - JoinUtil.JoinResult joinResult; if (existance == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + hashSetResult.setNoMatch(); } else { - joinResult = JoinUtil.JoinResult.MATCH; + hashSetResult.setMatch(); } - - optimizedHashSetResult.setJoinResult(joinResult); - - return joinResult; } public VectorMapJoinFastBytesHashSet( - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); keyStore = new VectorMapJoinFastKeyStore(writeBuffersSize); } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashTable.java index 6b536f0..2dd70cb 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashTable.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashTable.java @@ -22,7 +22,6 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashTable; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.io.BytesWritable; import org.apache.hive.common.util.HashCodeUtil; @@ -32,9 +31,7 @@ /* * An single byte array value hash map optimized for vector map join. */ -public abstract class VectorMapJoinFastBytesHashTable - extends VectorMapJoinFastHashTable - implements VectorMapJoinBytesHashTable { +public abstract class VectorMapJoinFastBytesHashTable extends VectorMapJoinFastHashTable { private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastBytesHashTable.class); @@ -42,59 +39,50 @@ protected VectorMapJoinFastKeyStore keyStore; - private BytesWritable testKeyBytesWritable; private BytesWritable testValueBytesWritable; - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - // No deserialization of key(s) here -- just get reference to bytes. - byte[] keyBytes = currentKey.getBytes(); - int keyLength = currentKey.getLength(); - add(keyBytes, 0, keyLength, currentValue); - } - @VisibleForTesting public void putRow(byte[] currentKey, byte[] currentValue) throws HiveException, IOException { - if (testKeyBytesWritable == null) { - testKeyBytesWritable = new BytesWritable(); + if (testValueBytesWritable == null) { testValueBytesWritable = new BytesWritable(); } - testKeyBytesWritable.set(currentKey, 0, currentKey.length); testValueBytesWritable.set(currentValue, 0, currentValue.length); - putRow(testKeyBytesWritable, testValueBytesWritable); + int hashCode = HashCodeUtil.murmurHash(currentKey, 0, currentKey.length); + add(currentKey, 0, currentKey.length, hashCode, testValueBytesWritable); } protected abstract void assignSlot(int slot, byte[] keyBytes, int keyStart, int keyLength, - long hashCode, boolean isNewKey, BytesWritable currentValue); + long longHashCode, boolean isNewKey, BytesWritable currentValue); - public void add(byte[] keyBytes, int keyStart, int keyLength, BytesWritable currentValue) { + public void add(byte[] keyBytes, int keyStart, int keyLength, int hashCode, BytesWritable currentValue) { if (resizeThreshold <= keysAssigned) { expandAndRehash(); } - long hashCode = HashCodeUtil.murmurHash(keyBytes, keyStart, keyLength); - int intHashCode = (int) hashCode; - int slot = (intHashCode & logicalHashBucketMask); + int slot = (hashCode & logicalHashBucketMask); long probeSlot = slot; int i = 0; boolean isNewKey; + long longHashCode = (0xFFFFFFFFL & (long) hashCode); // No sign extension. while (true) { int tripleIndex = 3 * slot; if (slotTriples[tripleIndex] == 0) { - // LOG.debug("VectorMapJoinFastBytesHashMap findWriteSlot slot " + slot + " tripleIndex " + tripleIndex + " empty"); + // LOG.info("VectorMapJoinFastBytesHashMap findWriteSlot slot " + slot + " tripleIndex " + tripleIndex + " empty longHashCode " + Long.toHexString(longHashCode)); isNewKey = true;; break; } - if (hashCode == slotTriples[tripleIndex + 1] && + if (longHashCode == slotTriples[tripleIndex + 1] && keyStore.equalKey(slotTriples[tripleIndex], keyBytes, keyStart, keyLength)) { - // LOG.debug("VectorMapJoinFastBytesHashMap findWriteSlot slot " + slot + " tripleIndex " + tripleIndex + " existing"); + // LOG.info("VectorMapJoinFastBytesHashMap findWriteSlot slot " + slot + " tripleIndex " + tripleIndex + " existing longHashCode " + Long.toHexString(longHashCode)); isNewKey = false; break; + } else { + // LOG.info("VectorMapJoinFastBytesHashMap findWriteSlot slot " + slot + " tripleIndex " + tripleIndex + " no match longHashCode " + Long.toHexString(longHashCode)); } - // TODO - ++metricPutConflict; + // Some other key (collision) - keep probing. + metricPutConflict++; probeSlot += (++i); slot = (int) (probeSlot & logicalHashBucketMask); } @@ -107,16 +95,18 @@ public void add(byte[] keyBytes, int keyStart, int keyLength, BytesWritable curr // debugDumpKeyProbe(keyOffset, keyLength, hashCode, slot); } - assignSlot(slot, keyBytes, keyStart, keyLength, hashCode, isNewKey, currentValue); + assignSlot(slot, keyBytes, keyStart, keyLength, longHashCode, isNewKey, currentValue); if (isNewKey) { keysAssigned++; } } - private void expandAndRehash() { + @Override + protected void expandAndRehashImpl(int capacity) { - int newLogicalHashBucketCount = logicalHashBucketCount * 2; + long expandTime = System.currentTimeMillis(); + int newLogicalHashBucketCount = capacity; int newLogicalHashBucketMask = newLogicalHashBucketCount - 1; int newMetricPutConflict = 0; int newLargestNumberOfSteps = 0; @@ -128,11 +118,11 @@ private void expandAndRehash() { int tripleIndex = slot * 3; long keyRef = slotTriples[tripleIndex]; if (keyRef != 0) { - long hashCode = slotTriples[tripleIndex + 1]; + long longHashCode = slotTriples[tripleIndex + 1]; long valueRef = slotTriples[tripleIndex + 2]; // Copy to new slot table. - int intHashCode = (int) hashCode; + int intHashCode = (int) longHashCode; int newSlot = intHashCode & newLogicalHashBucketMask; long newProbeSlot = newSlot; int newTripleIndex; @@ -161,7 +151,7 @@ private void expandAndRehash() { // LOG.debug("VectorMapJoinFastLongHashTable expandAndRehash key " + tableKey + " slot " + newSlot + " newPairIndex " + newPairIndex + " empty slot (i = " + i + ")"); newSlotTriples[newTripleIndex] = keyRef; - newSlotTriples[newTripleIndex + 1] = hashCode; + newSlotTriples[newTripleIndex + 1] = longHashCode; newSlotTriples[newTripleIndex + 2] = valueRef; } } @@ -172,27 +162,29 @@ private void expandAndRehash() { metricPutConflict = newMetricPutConflict; largestNumberOfSteps = newLargestNumberOfSteps; resizeThreshold = (int)(logicalHashBucketCount * loadFactor); + metricExpandsMs += (System.currentTimeMillis() - expandTime); metricExpands++; - // LOG.debug("VectorMapJoinFastLongHashTable expandAndRehash new logicalHashBucketCount " + logicalHashBucketCount + " resizeThreshold " + resizeThreshold + " metricExpands " + metricExpands); } - protected long findReadSlot(byte[] keyBytes, int keyStart, int keyLength, long hashCode) { + protected long findReadSlot(byte[] keyBytes, int keyStart, int keyLength, int hashCode) { - int intHashCode = (int) hashCode; - int slot = (intHashCode & logicalHashBucketMask); + int slot = (hashCode & logicalHashBucketMask); long probeSlot = slot; int i = 0; + long longHashCode = (0xFFFFFFFFL & (long) hashCode); // No sign extension. while (true) { int tripleIndex = slot * 3; - // LOG.debug("VectorMapJoinFastBytesHashMap findReadSlot slot keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " hashCode " + Long.toHexString(hashCode) + " entry hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); - if (slotTriples[tripleIndex] != 0 && hashCode == slotTriples[tripleIndex + 1]) { + // LOG.info("VectorMapJoinFastBytesHashMap findReadSlot slot keyRefWord " + Long.toHexString(slotTriples[tripleIndex]) + " longHashCode " + Long.toHexString(longHashCode) + " entry hashCode " + Long.toHexString(slotTriples[tripleIndex + 1]) + " valueRefWord " + Long.toHexString(slotTriples[tripleIndex + 2])); + if (slotTriples[tripleIndex] != 0 && longHashCode == slotTriples[tripleIndex + 1]) { // Finally, verify the key bytes match. + // LOG.info("VectorMapJoinFastBytesHashMap findReadSlot verify match..."); if (keyStore.equalKey(slotTriples[tripleIndex], keyBytes, keyStart, keyLength)) { return slotTriples[tripleIndex + 2]; } } // Some other key (collision) - keep probing. + metricGetConflict++; probeSlot += (++i); if (i > largestNumberOfSteps) { // We know we never went that far when we were inserting. @@ -217,8 +209,19 @@ private void allocateBucketArray() { } public VectorMapJoinFastBytesHashTable( - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); allocateBucketArray(); } + + @Override + public long memorySize() { + return keyStore.writeBuffers().size() + slotTriples.length * (Long.SIZE/Byte.SIZE) + 100; + } + + @Override + public void clear() { + // UNDONE + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashUtil.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashUtil.java index 80126ad..d0e303e 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashUtil.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastBytesHashUtil.java @@ -18,8 +18,6 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import org.apache.hadoop.hive.serde2.WriteBuffers; - public class VectorMapJoinFastBytesHashUtil { public static String displayBytes(byte[] bytes, int start, int length) { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMap.java index 262b619..836d0d8 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMap.java @@ -18,21 +18,12 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; - -public abstract class VectorMapJoinFastHashMap - extends VectorMapJoinFastHashTable - implements VectorMapJoinHashMap { - - @Override - public VectorMapJoinHashMapResult createHashMapResult() { - return new VectorMapJoinFastValueStore.HashMapResult(); - } +public abstract class VectorMapJoinFastHashMap extends VectorMapJoinFastHashTable { public VectorMapJoinFastHashMap( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMultiSet.java index 5f7c6a7..a0c11e1 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMultiSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashMultiSet.java @@ -18,31 +18,12 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; - -public abstract class VectorMapJoinFastHashMultiSet - extends VectorMapJoinFastHashTable implements VectorMapJoinHashMultiSet { - - @Override - public VectorMapJoinHashMultiSetResult createHashMultiSetResult() { - return new HashMultiSetResult(); - } - - public static class HashMultiSetResult extends VectorMapJoinHashMultiSetResult { - - HashMultiSetResult() { - super(); - } - - public void set(long count) { - this.count = count; - } - } +public abstract class VectorMapJoinFastHashMultiSet extends VectorMapJoinFastHashTable { public VectorMapJoinFastHashMultiSet( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashSet.java index 8509971..d85d5c8 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashSet.java @@ -18,27 +18,12 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; - -public abstract class VectorMapJoinFastHashSet - extends VectorMapJoinFastHashTable implements VectorMapJoinHashSet { - - @Override - public VectorMapJoinHashSetResult createHashSetResult() { - return new HashSetResult(); - } - - public static class HashSetResult extends VectorMapJoinHashSetResult { - - HashSetResult() { - super(); - } - } +public abstract class VectorMapJoinFastHashSet extends VectorMapJoinFastHashTable { public VectorMapJoinFastHashSet( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTable.java index 099f38e..e0ab8cf 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTable.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTable.java @@ -18,24 +18,41 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import java.io.IOException; + import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.BytesWritable; + +public abstract class VectorMapJoinFastHashTable implements MapJoinHashTable { -public abstract class VectorMapJoinFastHashTable implements VectorMapJoinHashTable { public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastHashTable.class); + protected VectorMapJoinFastHashTableFactory mapJoinHashTableFactory; + protected int logicalHashBucketCount; protected int logicalHashBucketMask; protected float loadFactor; protected int writeBuffersSize; - protected int metricPutConflict; protected int largestNumberOfSteps; protected int keysAssigned; + protected int numValues; protected int resizeThreshold; + + protected int metricPutConflict; + protected int metricGetConflict; protected int metricExpands; + protected int metricExpandsMs; + private static void validateCapacity(long capacity) { if (Long.bitCount(capacity) != 1) { @@ -46,12 +63,15 @@ private static void validateCapacity(long capacity) { } } - private static int nextHighestPowerOfTwo(int v) { + protected static int nextHighestPowerOfTwo(int v) { return Integer.highestOneBit(v) << 1; } public VectorMapJoinFastHashTable( - int initialCapacity, float loadFactor, int writeBuffersSize) { + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + + this.mapJoinHashTableFactory = mapJoinHashTableFactory; initialCapacity = (Long.bitCount(initialCapacity) == 1) ? initialCapacity : nextHighestPowerOfTwo(initialCapacity); @@ -64,10 +84,150 @@ public VectorMapJoinFastHashTable( this.loadFactor = loadFactor; this.writeBuffersSize = writeBuffersSize; + + keysAssigned = 0; + numValues = 0; + + metricPutConflict = 0; + metricGetConflict = 0; + metricExpands = 0; + metricExpandsMs= 0; + + } + + protected void expandAndRehash() { + expandAndRehashImpl(logicalHashBucketCount << 1); + } + + protected abstract void expandAndRehashImpl(int capacity); + + @Override + public void expandAndRehashToTarget(int estimateNewRowCount) { + int oldCount = logicalHashBucketCount; + int newCount = oldCount + estimateNewRowCount; + if (resizeThreshold <= newCount) { + newCount = + (Long.bitCount(newCount) == 1) ? estimateNewRowCount : nextHighestPowerOfTwo(newCount); + expandAndRehashImpl(newCount); + LOG.info("Expand and rehash to " + newCount + " from " + oldCount); + } } + /** + * Number of keys in the hashmap + * @return number of keys + */ @Override public int size() { return keysAssigned; } + + /** + * Number of values in the hashmap + * This is equal to or bigger than number of keys, since some values may share the same key + * @return number of values + */ + @Override + public int getNumValues() { + return numValues; + } + + @Override + public void seal() { + // Nothing to seal in base class. + } + + @Override + public void clear() { + // This will make the object completely unusable. Semantics of clear are not defined... + this.keysAssigned = 0; + this.numValues = 0; + } + + //---------------------------- COMMON LONG METHODS (Begin)---------------------------------------- + + @Override + public boolean useMinMax() { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public long min() { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public long max() { + throw new RuntimeException("Expected this method to be overriden"); + } + + //----------------------------- COMMON LONG METHODS (End)----------------------------------------- + + //-------------------------------- HASH MAP (Begin)----------------------------------------------- + + @Override + public void hashMapLookup(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMapResult hashMapResult) + throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public void hashMapLookup(long key, int hashCode, + MapJoinHashMapResult hashMapResult) throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + //-------------------------------- HASH MAP (End) ------------------------------------------------ + + //---------------------------- HASH MULTI-SET (Begin) ------------------------------------------- + + @Override + public void hashMultiSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) + throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public void hashMultiSetContains(long key, int hashCode, + MapJoinHashMultiSetResult hashMultiSetResult) throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + //----------------------------- HASH MULTI-SET (End) -------------------------------------------- + + //------------------------------- HASH SET (Begin) ---------------------------------------------- + + @Override + public void hashSetContains(byte[] keyBytes, int keyStart, int keyLength, int hashCode, + MapJoinHashSetResult hashSetResult) throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public void hashSetContains(long key, int hashCode, + MapJoinHashSetResult hashSetResult) throws IOException { + throw new RuntimeException("Expected this method to be overriden"); + } + + //--------------------------------- HASH SET (End) ---------------------------------------------- + + @Override + public void put(KeyValuePut keyValuePut) throws SerDeException { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public long memorySize() { + throw new RuntimeException("Expected this method to be overriden"); + } + + @Override + public void debugDumpMetrics() { + LOG.info("Map metrics: keys allocated " + logicalHashBucketCount +", keys assigned " + keysAssigned + + ", write conflict " + metricPutConflict + ", write max dist " + largestNumberOfSteps + + ", read conflict " + metricGetConflict + + ", expanded " + metricExpands + " times in " + metricExpandsMs + "ms"); + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableFactory.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableFactory.java new file mode 100644 index 0000000..5c6eb2d --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableFactory.java @@ -0,0 +1,202 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResultImpl; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResultImpl; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableFactory; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.plan.MapJoinDesc; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKind; + +import com.google.common.annotations.VisibleForTesting; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public class VectorMapJoinFastHashTableFactory implements MapJoinHashTableFactory { + + private final boolean isOuterJoin; + private final HashTableKind hashTableKind; + private final HashTableKeyType hashTableKeyType; + private final boolean useMinMax; + + @VisibleForTesting + public VectorMapJoinFastHashTableFactory(HashTableKeyType hashTableKeyType) { + isOuterJoin = false; + hashTableKind = HashTableKind.HASH_MAP; + this.hashTableKeyType = hashTableKeyType; + useMinMax = false; + } + + public VectorMapJoinFastHashTableFactory(MapJoinDesc desc) { + + VectorMapJoinDesc vectorDesc = desc.getVectorDesc(); + + isOuterJoin = !desc.isNoOuterJoin(); + hashTableKind = vectorDesc.hashTableKind(); + hashTableKeyType = vectorDesc.hashTableKeyType(); + useMinMax = vectorDesc.minMaxEnabled() && + (hashTableKeyType == HashTableKeyType.BOOLEAN || + hashTableKeyType == HashTableKeyType.BYTE || + hashTableKeyType == HashTableKeyType.SHORT || + hashTableKeyType == HashTableKeyType.INT || + hashTableKeyType == HashTableKeyType.LONG); + } + + @Override + public MapJoinHashTable createHashTable(int initialCapacity, float loadFactor, + int writeBuffersSize, int maxProbeSize) { + + MapJoinHashTable MapJoinHashTableFind = null; + switch (hashTableKeyType) { + case BOOLEAN: + case BYTE: + case SHORT: + case INT: + case LONG: + switch (hashTableKind) { + case HASH_MAP: + MapJoinHashTableFind = new VectorMapJoinFastLongHashMap( + this, + useMinMax, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_MULTISET: + MapJoinHashTableFind = new VectorMapJoinFastLongHashMultiSet( + this, + useMinMax, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_SET: + MapJoinHashTableFind = new VectorMapJoinFastLongHashSet( + this, + useMinMax, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + } + break; + + case STRING: + switch (hashTableKind) { + case HASH_MAP: + MapJoinHashTableFind = new VectorMapJoinFastStringHashMap( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_MULTISET: + MapJoinHashTableFind = new VectorMapJoinFastStringHashMultiSet( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_SET: + MapJoinHashTableFind = new VectorMapJoinFastStringHashSet( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + } + break; + + case MULTI_KEY: + switch (hashTableKind) { + case HASH_MAP: + MapJoinHashTableFind = new VectorMapJoinFastMultiKeyHashMap( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_MULTISET: + MapJoinHashTableFind = new VectorMapJoinFastMultiKeyHashMultiSet( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + case HASH_SET: + MapJoinHashTableFind = new VectorMapJoinFastMultiKeyHashSet( + this, + isOuterJoin, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + break; + } + break; + default: + throw new RuntimeException("Unexpected vector map join hash table key type " + hashTableKeyType.name()); + } + + return MapJoinHashTableFind; + } + + /* + * @return A new hash map result implementation specific object. + * + * The object can be used to access the values when there is a match, or + * access spill information when the partition with the key is currently spilled. + */ + @Override + public MapJoinHashMapResult createHashMapResult() { + return new VectorMapJoinFastValueStore.HashMapResult(); + } + + /* + * @return A new hash multi-set result implementation specific object. + * + * The object can be used to access the *count* of values when the key is contained in the + * multi-set, or access spill information when the partition with the key is currently spilled. + */ + @Override + public MapJoinHashMultiSetResult createHashMultiSetResult() { + return new MapJoinHashMultiSetResultImpl(); + } + + /* + * @return A new hash set result implementation specific object. + * + * The object can be used to access access spill information when the partition with the key + * is currently spilled. + */ + @Override + public MapJoinHashSetResult createHashSetResult() { + return new MapJoinHashSetResultImpl(); + } + + @Override + public boolean keyValuePutHelperIsExternal() { + return true; + } + + @Override + public KeyValuePut createKeyValuePutHelper() { + return new VectorMapJoinFastKeyValuePut(hashTableKind, hashTableKeyType, isOuterJoin); + } + + @Override + public boolean useMinMax() { + return useMinMax; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableLoader.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableLoader.java deleted file mode 100644 index 09a1ffc..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastHashTableLoader.java +++ /dev/null @@ -1,114 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; - -import java.io.IOException; -import java.util.Collections; -import java.util.Map; - -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; -import org.apache.hadoop.conf.Configuration; -import org.apache.hadoop.hive.conf.HiveConf; -import org.apache.hadoop.hive.ql.exec.MapJoinOperator; -import org.apache.hadoop.hive.ql.exec.MapredContext; -import org.apache.hadoop.hive.ql.exec.mr.ExecMapperContext; -import org.apache.hadoop.hive.ql.exec.persistence.HashMapWrapper; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainerSerDe; -import org.apache.hadoop.hive.ql.exec.tez.TezContext; -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.ql.plan.MapJoinDesc; -import org.apache.hadoop.hive.serde2.SerDeException; -import org.apache.hadoop.io.BytesWritable; -import org.apache.tez.runtime.api.Input; -import org.apache.tez.runtime.api.LogicalInput; -import org.apache.tez.runtime.library.api.KeyValueReader; - -/** - * HashTableLoader for Tez constructs the hashtable from records read from - * a broadcast edge. - */ -public class VectorMapJoinFastHashTableLoader implements org.apache.hadoop.hive.ql.exec.HashTableLoader { - - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastHashTableLoader.class.getName()); - - private Configuration hconf; - protected MapJoinDesc desc; - private TezContext tezContext; - - @Override - public void init(ExecMapperContext context, MapredContext mrContext, - Configuration hconf, MapJoinOperator joinOp) { - this.tezContext = (TezContext) mrContext; - this.hconf = hconf; - this.desc = joinOp.getConf(); - } - - @Override - public void load(MapJoinTableContainer[] mapJoinTables, - MapJoinTableContainerSerDe[] mapJoinTableSerdes) - throws HiveException { - - Map parentToInput = desc.getParentToInput(); - Map parentKeyCounts = desc.getParentKeyCounts(); - - for (int pos = 0; pos < mapJoinTables.length; pos++) { - if (pos == desc.getPosBigTable()) { - continue; - } - - String inputName = parentToInput.get(pos); - LogicalInput input = tezContext.getInput(inputName); - - try { - input.start(); - tezContext.getTezProcessorContext().waitForAnyInputReady( - Collections. singletonList(input)); - } catch (Exception e) { - throw new HiveException(e); - } - - try { - KeyValueReader kvReader = (KeyValueReader) input.getReader(); - - Long keyCountObj = parentKeyCounts.get(pos); - long keyCount = (keyCountObj == null) ? -1 : keyCountObj.longValue(); - - VectorMapJoinFastTableContainer vectorMapJoinFastTableContainer = - new VectorMapJoinFastTableContainer(desc, hconf, keyCount); - - while (kvReader.next()) { - vectorMapJoinFastTableContainer.putRow( - null, (BytesWritable) kvReader.getCurrentKey(), - null, (BytesWritable) kvReader.getCurrentValue()); - } - - vectorMapJoinFastTableContainer.seal(); - mapJoinTables[pos] = (MapJoinTableContainer) vectorMapJoinFastTableContainer; - - } catch (IOException e) { - throw new HiveException(e); - } catch (SerDeException e) { - throw new HiveException(e); - } catch (Exception e) { - throw new HiveException(e); - } - } - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyStore.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyStore.java index efdcd43..bca4ef8 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyStore.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyStore.java @@ -20,6 +20,7 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; +import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr; import org.apache.hadoop.hive.serde2.WriteBuffers; // Optimized for sequential key lookup. @@ -149,6 +150,10 @@ public boolean equalKey(long keyRefWord, byte[] keyBytes, int keyStart, int keyL return true; } + public WriteBuffers writeBuffers() { + return writeBuffers; + } + public VectorMapJoinFastKeyStore(int writeBuffersSize) { writeBuffers = new WriteBuffers(writeBuffersSize, AbsoluteKeyOffset.maxSize); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyValuePut.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyValuePut.java new file mode 100644 index 0000000..5755639 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastKeyValuePut.java @@ -0,0 +1,197 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePut; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKind; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableDeserializeRead; +import org.apache.hadoop.hive.serde2.fast.DeserializeRead.ReadStringResults; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory; +import org.apache.hadoop.io.BytesWritable; +import org.apache.hadoop.io.Writable; +import org.apache.hive.common.util.HashCodeUtil; + +/* + * Root abstract class for a vector map join hash table (which could be a hash map, hash multi-set, + * or hash set). + */ +public class VectorMapJoinFastKeyValuePut implements KeyValuePut { + + private final HashTableKind hashTableKind; + private final HashTableKeyType hashTableKeyType; + private final boolean isOuterJoin; + + private final BinarySortableDeserializeRead keyBinarySortableDeserializeRead; + private final ReadStringResults readStringResults; + + private final boolean isLong; + + private BytesWritable keyBytesWritable; + private BytesWritable valueBytesWritable; + + private long longKey; + + private boolean hasHashCode; + private int hashCode; + + private boolean isNull; + + public VectorMapJoinFastKeyValuePut(HashTableKind hashTableKind, + HashTableKeyType hashTableKeyType, boolean isOuterJoin) { + this.hashTableKind = hashTableKind; + this.hashTableKeyType = hashTableKeyType; + this.isOuterJoin = isOuterJoin; + + switch (hashTableKeyType) { + case BOOLEAN: + case BYTE: + case SHORT: + case INT: + case LONG: + { + isLong = true; + TypeInfo typeInfo; + switch (hashTableKeyType) { + case BOOLEAN: + typeInfo = TypeInfoFactory.booleanTypeInfo; + break; + case BYTE: + typeInfo = TypeInfoFactory.byteTypeInfo; + break; + case SHORT: + typeInfo = TypeInfoFactory.shortTypeInfo; + break; + case INT: + typeInfo = TypeInfoFactory.intTypeInfo; + break; + case LONG: + typeInfo = TypeInfoFactory.longTypeInfo; + break; + default: + throw new RuntimeException("Unexpected vector map join hash table key type " + hashTableKeyType.name()); + } + TypeInfo[] typeInfos = { typeInfo }; + keyBinarySortableDeserializeRead = new BinarySortableDeserializeRead(typeInfos); + readStringResults = null; + } + break; + case STRING: + { + isLong = false; + TypeInfo[] typeInfos = { TypeInfoFactory.stringTypeInfo }; + keyBinarySortableDeserializeRead = new BinarySortableDeserializeRead(typeInfos); + readStringResults = keyBinarySortableDeserializeRead.createReadStringResults(); + } + break; + case MULTI_KEY: + isLong = false; + keyBinarySortableDeserializeRead = null; + readStringResults = null; + break; + default: + throw new RuntimeException("Unexpected vector map join hash table key type " + hashTableKeyType.name()); + } + + hasHashCode = false; + } + + @Override + public void setKeyValue(Writable keyWritable, Writable valueWritable) + throws SerDeException, IOException { + + keyBytesWritable = (BytesWritable) keyWritable; + valueBytesWritable = (BytesWritable) valueWritable; + isNull = false; // Assume. + hasHashCode = false; + + if (isLong) { + // Deserialized the single long column. + keyBinarySortableDeserializeRead.set(keyBytesWritable.getBytes(), 0, + keyBytesWritable.getLength()); + if (keyBinarySortableDeserializeRead.readCheckNull()) { + isNull = true; + return; + } + longKey = VectorMapJoinFastLongHashUtil.deserializeLongKey( + keyBinarySortableDeserializeRead, hashTableKeyType); + hashCode = HashCodeUtil.calculateLongHashCode(longKey); + } else { + switch (hashTableKeyType) { + case STRING: + { + // Deserialize the single string column. + keyBinarySortableDeserializeRead.set(keyBytesWritable.getBytes(), 0, + keyBytesWritable.getLength()); + if (keyBinarySortableDeserializeRead.readCheckNull()) { + isNull = true; + return; + } + keyBinarySortableDeserializeRead.readString(readStringResults); + hashCode = HashCodeUtil.murmurHash(readStringResults.bytes, readStringResults.start, + readStringResults.length); + } + break; + + case MULTI_KEY: + // Leave the multi-key unserialized. And, let all NULL entries into the small table. + hashCode = HashCodeUtil.murmurHash(keyBytesWritable.getBytes(), 0, keyBytesWritable.getLength()); + break; + default: + throw new RuntimeException("Unexpected vector map join hash table key type " + hashTableKeyType.name()); + } + hasHashCode = true; + } + } + + @Override + public boolean hasHashCode() { + return hasHashCode; + } + + @Override + public int getKeyHashCode() throws SerDeException { + return hashCode; + } + + public boolean isNull() { + return isNull; + } + + @Override + public long getLongKey() { + return longKey; + } + + public ReadStringResults getStringReadStringResults() { + return readStringResults; + } + + public BytesWritable getKeyBytesWritable() { + return keyBytesWritable; + } + + public BytesWritable getValueBytesWritable() { + return valueBytesWritable; + } +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMap.java index 1384fc9..7291bf9 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMap.java @@ -20,27 +20,30 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMap; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single long value map optimized for vector map join. */ -public class VectorMapJoinFastLongHashMap - extends VectorMapJoinFastLongHashTable - implements VectorMapJoinLongHashMap { +public class VectorMapJoinFastLongHashMap extends VectorMapJoinFastLongHashTable { public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastLongHashMap.class); protected VectorMapJoinFastValueStore valueStore; @Override - public VectorMapJoinHashMapResult createHashMapResult() { - return new VectorMapJoinFastValueStore.HashMapResult(); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + add(internalKeyValuePut.getLongKey(), internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } @Override @@ -61,35 +64,33 @@ public void assignSlot(int slot, long key, boolean isNewKey, BytesWritable curre } @Override - public JoinUtil.JoinResult lookup(long key, VectorMapJoinHashMapResult hashMapResult) { + public void hashMapLookup(long key, int hashCode, MapJoinHashMapResult hashMapResult) { VectorMapJoinFastValueStore.HashMapResult optimizedHashMapResult = (VectorMapJoinFastValueStore.HashMapResult) hashMapResult; optimizedHashMapResult.forget(); - long hashCode = HashCodeUtil.calculateLongHashCode(key); // LOG.debug("VectorMapJoinFastLongHashMap lookup " + key + " hashCode " + hashCode); long valueRef = findReadSlot(key, hashCode); - JoinUtil.JoinResult joinResult; if (valueRef == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + optimizedHashMapResult.setNoMatch(); } else { - optimizedHashMapResult.set(valueStore, valueRef); - - joinResult = JoinUtil.JoinResult.MATCH; + optimizedHashMapResult.setMatch(valueStore, valueRef); } - - optimizedHashMapResult.setJoinResult(joinResult); - - return joinResult; } public VectorMapJoinFastLongHashMap( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(minMaxEnabled, isOuterJoin, hashTableKeyType, - initialCapacity, loadFactor, writeBuffersSize); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, minMaxEnabled, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); valueStore = new VectorMapJoinFastValueStore(writeBuffersSize); } + + @Override + public long memorySize() { + return valueStore.writeBuffers().size() + slotPairs.length * (Long.SIZE/Byte.SIZE) + 100; + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMultiSet.java index 94bf706..3af43b8 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMultiSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashMultiSet.java @@ -18,31 +18,30 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import java.io.IOException; - import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMultiSet; -import org.apache.hadoop.hive.ql.metadata.HiveException; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMultiSetResult; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single long value multi-set optimized for vector map join. */ -public class VectorMapJoinFastLongHashMultiSet - extends VectorMapJoinFastLongHashTable - implements VectorMapJoinLongHashMultiSet { +public class VectorMapJoinFastLongHashMultiSet extends VectorMapJoinFastLongHashTable { public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastLongHashMultiSet.class); @Override - public VectorMapJoinHashMultiSetResult createHashMultiSetResult() { - return new VectorMapJoinFastHashMultiSet.HashMultiSetResult(); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + add(internalKeyValuePut.getLongKey(), internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } @Override @@ -61,32 +60,28 @@ public void assignSlot(int slot, long key, boolean isNewKey, BytesWritable curre @Override - public JoinUtil.JoinResult contains(long key, VectorMapJoinHashMultiSetResult hashMultiSetResult) { - - VectorMapJoinFastHashMultiSet.HashMultiSetResult optimizedHashMultiSetResult = - (VectorMapJoinFastHashMultiSet.HashMultiSetResult) hashMultiSetResult; + public void hashMultiSetContains(long key, int hashCode, MapJoinHashMultiSetResult hashMultiSetResult) { - optimizedHashMultiSetResult.forget(); + hashMultiSetResult.forget(); - long hashCode = HashCodeUtil.calculateLongHashCode(key); long count = findReadSlot(key, hashCode); - JoinUtil.JoinResult joinResult; if (count == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + hashMultiSetResult.setNoMatch(); } else { - optimizedHashMultiSetResult.set(count); - joinResult = JoinUtil.JoinResult.MATCH; + hashMultiSetResult.setMatch(count); } - - optimizedHashMultiSetResult.setJoinResult(joinResult); - - return joinResult; } public VectorMapJoinFastLongHashMultiSet( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(minMaxEnabled, isOuterJoin, hashTableKeyType, - initialCapacity, loadFactor, writeBuffersSize); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, minMaxEnabled, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + } + + @Override + public long memorySize() { + return slotPairs.length * (Long.SIZE/Byte.SIZE) + 100; } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashSet.java index 2cbc548..15e44c4 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashSet.java @@ -20,26 +20,28 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashSet; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashSetResult; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.io.BytesWritable; -import org.apache.hive.common.util.HashCodeUtil; /* * An single long value multi-set optimized for vector map join. */ -public class VectorMapJoinFastLongHashSet - extends VectorMapJoinFastLongHashTable - implements VectorMapJoinLongHashSet { +public class VectorMapJoinFastLongHashSet extends VectorMapJoinFastLongHashTable { public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastLongHashSet.class); @Override - public VectorMapJoinHashSetResult createHashSetResult() { - return new VectorMapJoinFastHashSet.HashSetResult(); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + add(internalKeyValuePut.getLongKey(), internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } @Override @@ -54,32 +56,29 @@ public void assignSlot(int slot, long key, boolean isNewKey, BytesWritable curre } @Override - public JoinResult contains(long key, VectorMapJoinHashSetResult hashSetResult) { + public void hashSetContains(long key, int hashCode, MapJoinHashSetResult hashSetResult) { - VectorMapJoinFastHashSet.HashSetResult optimizedHashSetResult = - (VectorMapJoinFastHashSet.HashSetResult) hashSetResult; + hashSetResult.forget(); - optimizedHashSetResult.forget(); - - long hashCode = HashCodeUtil.calculateLongHashCode(key); long existance = findReadSlot(key, hashCode); - JoinUtil.JoinResult joinResult; if (existance == -1) { - joinResult = JoinUtil.JoinResult.NOMATCH; + hashSetResult.setNoMatch(); } else { - joinResult = JoinUtil.JoinResult.MATCH; + hashSetResult.setMatch(); } - optimizedHashSetResult.setJoinResult(joinResult); - - return joinResult; - } public VectorMapJoinFastLongHashSet( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(minMaxEnabled, isOuterJoin, hashTableKeyType, - initialCapacity, loadFactor, writeBuffersSize); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, minMaxEnabled, isOuterJoin, hashTableKeyType, + initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); + } + + @Override + public long memorySize() { + return slotPairs.length * (Long.SIZE/Byte.SIZE) + 100; } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashTable.java index f37f056..836a6db 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashTable.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastLongHashTable.java @@ -22,10 +22,6 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashTable; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableDeserializeRead; @@ -33,16 +29,13 @@ import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory; import org.apache.hadoop.io.BytesWritable; import org.apache.hive.common.util.HashCodeUtil; -import org.apache.tez.runtime.library.api.KeyValueReader; import com.google.common.annotations.VisibleForTesting; /* * An single long value map optimized for vector map join. */ -public abstract class VectorMapJoinFastLongHashTable - extends VectorMapJoinFastHashTable - implements VectorMapJoinLongHashTable { +public abstract class VectorMapJoinFastLongHashTable extends VectorMapJoinFastHashTable { public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastLongHashTable.class); @@ -75,43 +68,26 @@ public long max() { return max; } - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - byte[] keyBytes = currentKey.getBytes(); - int keyLength = currentKey.getLength(); - keyBinarySortableDeserializeRead.set(keyBytes, 0, keyLength); - if (keyBinarySortableDeserializeRead.readCheckNull()) { - return; - } - - long key = VectorMapJoinFastLongHashUtil.deserializeLongKey( - keyBinarySortableDeserializeRead, hashTableKeyType); - - add(key, currentValue); - } - - @VisibleForTesting public void putRow(long currentKey, byte[] currentValue) throws HiveException, IOException { if (testValueBytesWritable == null) { testValueBytesWritable = new BytesWritable(); } testValueBytesWritable.set(currentValue, 0, currentValue.length); - add(currentKey, testValueBytesWritable); + int hashCode = HashCodeUtil.calculateLongHashCode(currentKey); + add(currentKey, hashCode, testValueBytesWritable); } protected abstract void assignSlot(int slot, long key, boolean isNewKey, BytesWritable currentValue); - public void add(long key, BytesWritable currentValue) { + public void add(long key, int hashCode, BytesWritable currentValue) { if (resizeThreshold <= keysAssigned) { expandAndRehash(); } - long hashCode = HashCodeUtil.calculateLongHashCode(key); - int intHashCode = (int) hashCode; - int slot = (intHashCode & logicalHashBucketMask); + int slot = (hashCode & logicalHashBucketMask); long probeSlot = slot; int i = 0; boolean isNewKey; @@ -129,8 +105,9 @@ public void add(long key, BytesWritable currentValue) { isNewKey = false; break; } - ++metricPutConflict; + // Some other key (collision) - keep probing. + metricPutConflict++; probeSlot += (++i); slot = (int)(probeSlot & logicalHashBucketMask); } @@ -160,8 +137,10 @@ public void add(long key, BytesWritable currentValue) { } } - private void expandAndRehash() { + @Override + protected void expandAndRehashImpl(int capacity) { + long expandTime = System.currentTimeMillis(); int newLogicalHashBucketCount = logicalHashBucketCount * 2; int newLogicalHashBucketMask = newLogicalHashBucketCount - 1; int newMetricPutConflict = 0; @@ -177,9 +156,9 @@ private void expandAndRehash() { long tableKey = slotPairs[pairIndex + 1]; // Copy to new slot table. - long hashCode = HashCodeUtil.calculateLongHashCode(tableKey); - int intHashCode = (int) hashCode; - int newSlot = intHashCode & newLogicalHashBucketMask; + int hashCode = HashCodeUtil.calculateLongHashCode(tableKey); + + int newSlot = hashCode & newLogicalHashBucketMask; long newProbeSlot = newSlot; int newPairIndex; int i = 0; @@ -217,14 +196,13 @@ private void expandAndRehash() { metricPutConflict = newMetricPutConflict; largestNumberOfSteps = newLargestNumberOfSteps; resizeThreshold = (int)(logicalHashBucketCount * loadFactor); + metricExpandsMs += (System.currentTimeMillis() - expandTime); metricExpands++; - // LOG.debug("VectorMapJoinFastLongHashTable expandAndRehash new logicalHashBucketCount " + logicalHashBucketCount + " resizeThreshold " + resizeThreshold + " metricExpands " + metricExpands); } - protected long findReadSlot(long key, long hashCode) { + protected long findReadSlot(long key, int hashCode) { - int intHashCode = (int) hashCode; - int slot = intHashCode & logicalHashBucketMask; + int slot = hashCode & logicalHashBucketMask; long probeSlot = slot; int i = 0; @@ -267,9 +245,10 @@ private void allocateBucketArray() { } public VectorMapJoinFastLongHashTable( - boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); this.isOuterJoin = isOuterJoin; this.hashTableKeyType = hashTableKeyType; PrimitiveTypeInfo[] primitiveTypeInfos = { TypeInfoFactory.longTypeInfo }; @@ -279,4 +258,9 @@ public VectorMapJoinFastLongHashTable( min = Long.MAX_VALUE; max = Long.MIN_VALUE; } + + @Override + public void clear() { + // UNDONE + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMap.java index 9a9fb8d..fcc6f37 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMap.java @@ -18,22 +18,37 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.BytesWritable; + import com.google.common.annotations.VisibleForTesting; /* * An multi-key value hash map optimized for vector map join. */ -public class VectorMapJoinFastMultiKeyHashMap - extends VectorMapJoinFastBytesHashMap { +public class VectorMapJoinFastMultiKeyHashMap extends VectorMapJoinFastBytesHashMap { + + @Override + public void put(KeyValuePut keyValuePut) throws SerDeException { + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + + BytesWritable keyBytesWritable = internalKeyValuePut.getKeyBytesWritable(); + add(keyBytesWritable.getBytes(), 0, keyBytesWritable.getLength(), + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); + } @VisibleForTesting - public VectorMapJoinFastMultiKeyHashMap(int initialCapacity, float loadFactor, int wbSize) { - this(false, initialCapacity, loadFactor, wbSize); + public VectorMapJoinFastMultiKeyHashMap( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + int initialCapacity, float loadFactor, int wbSize, int maxProbeSize) { + this(mapJoinHashTableFactory, false, initialCapacity, loadFactor, wbSize, maxProbeSize); } public VectorMapJoinFastMultiKeyHashMap( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMultiSet.java index a8744a5..8160884 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMultiSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashMultiSet.java @@ -18,15 +18,29 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.BytesWritable; + /* * An multi-key value hash multi-set optimized for vector map join. */ public class VectorMapJoinFastMultiKeyHashMultiSet extends VectorMapJoinFastBytesHashMultiSet { + @Override + public void put(KeyValuePut keyValuePut) throws SerDeException { + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + + BytesWritable keyBytesWritable = internalKeyValuePut.getKeyBytesWritable(); + add(keyBytesWritable.getBytes(), 0, keyBytesWritable.getLength(), + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); + } + public VectorMapJoinFastMultiKeyHashMultiSet( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashSet.java index a8048e5..427c971 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastMultiKeyHashSet.java @@ -18,15 +18,29 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.BytesWritable; + /* * An multi-key value hash set optimized for vector map join. */ public class VectorMapJoinFastMultiKeyHashSet extends VectorMapJoinFastBytesHashSet { + @Override + public void put(KeyValuePut keyValuePut) throws SerDeException { + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + + BytesWritable keyBytesWritable = internalKeyValuePut.getKeyBytesWritable(); + add(keyBytesWritable.getBytes(), 0, keyBytesWritable.getLength(), + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); + } + public VectorMapJoinFastMultiKeyHashSet( - boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, + boolean isOuterJoin, + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringCommon.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringCommon.java deleted file mode 100644 index adb8044..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringCommon.java +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableDeserializeRead; -import org.apache.hadoop.hive.serde2.fast.DeserializeRead.ReadStringResults; -import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; -import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory; -import org.apache.hadoop.io.BytesWritable; -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; - -/* - * An single byte array value hash map optimized for vector map join. - */ -public class VectorMapJoinFastStringCommon { - - public static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinFastStringCommon.class); - - private boolean isOuterJoin; - - private BinarySortableDeserializeRead keyBinarySortableDeserializeRead; - - private ReadStringResults readStringResults; - - public void adaptPutRow(VectorMapJoinFastBytesHashTable hashTable, - BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - - byte[] keyBytes = currentKey.getBytes(); - int keyLength = currentKey.getLength(); - keyBinarySortableDeserializeRead.set(keyBytes, 0, keyLength); - if (keyBinarySortableDeserializeRead.readCheckNull()) { - return; - } - keyBinarySortableDeserializeRead.readString(readStringResults); - - hashTable.add(readStringResults.bytes, readStringResults.start, readStringResults.length, - currentValue); - } - - public VectorMapJoinFastStringCommon(boolean isOuterJoin) { - this.isOuterJoin = isOuterJoin; - PrimitiveTypeInfo[] primitiveTypeInfos = { TypeInfoFactory.stringTypeInfo }; - keyBinarySortableDeserializeRead = new BinarySortableDeserializeRead(primitiveTypeInfos); - readStringResults = keyBinarySortableDeserializeRead.createReadStringResults(); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMap.java index 6f181b2..2df31b9 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMap.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMap.java @@ -18,27 +18,32 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import java.io.IOException; - -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.io.BytesWritable; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.fast.DeserializeRead.ReadStringResults; /* * An single byte array value hash map optimized for vector map join. */ public class VectorMapJoinFastStringHashMap extends VectorMapJoinFastBytesHashMap { - private VectorMapJoinFastStringCommon stringCommon; - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - stringCommon.adaptPutRow(this, currentKey, currentValue); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + ReadStringResults readStringResults = internalKeyValuePut.getStringReadStringResults(); + add(readStringResults.bytes, readStringResults.start, readStringResults.length, + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } public VectorMapJoinFastStringHashMap( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); - stringCommon = new VectorMapJoinFastStringCommon(isOuterJoin); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMultiSet.java index 9653b71..0b42a5c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMultiSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashMultiSet.java @@ -18,27 +18,32 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import java.io.IOException; - -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.io.BytesWritable; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.fast.DeserializeRead.ReadStringResults; /* * An single byte array value hash map optimized for vector map join. */ public class VectorMapJoinFastStringHashMultiSet extends VectorMapJoinFastBytesHashMultiSet { - private VectorMapJoinFastStringCommon stringCommon; - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - stringCommon.adaptPutRow(this, currentKey, currentValue); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + ReadStringResults readStringResults = internalKeyValuePut.getStringReadStringResults(); + add(readStringResults.bytes, readStringResults.start, readStringResults.length, + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } public VectorMapJoinFastStringHashMultiSet( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); - stringCommon = new VectorMapJoinFastStringCommon(isOuterJoin); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashSet.java index 6419a0b..8d8b664 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashSet.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastStringHashSet.java @@ -18,27 +18,32 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; -import java.io.IOException; - -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.io.BytesWritable; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.hive.serde2.fast.DeserializeRead.ReadStringResults; /* * An single byte array value hash map optimized for vector map join. */ public class VectorMapJoinFastStringHashSet extends VectorMapJoinFastBytesHashSet { - private VectorMapJoinFastStringCommon stringCommon; - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) throws HiveException, IOException { - stringCommon.adaptPutRow(this, currentKey, currentValue); + public void put(KeyValuePut keyValuePut) throws SerDeException { + + VectorMapJoinFastKeyValuePut internalKeyValuePut = ((VectorMapJoinFastKeyValuePut) keyValuePut); + if (internalKeyValuePut.isNull()) { + return; + } + + ReadStringResults readStringResults = internalKeyValuePut.getStringReadStringResults(); + add(readStringResults.bytes, readStringResults.start, readStringResults.length, + internalKeyValuePut.getKeyHashCode(), + internalKeyValuePut.getValueBytesWritable()); } public VectorMapJoinFastStringHashSet( + VectorMapJoinFastHashTableFactory mapJoinHashTableFactory, boolean isOuterJoin, - int initialCapacity, float loadFactor, int writeBuffersSize) { - super(initialCapacity, loadFactor, writeBuffersSize); - stringCommon = new VectorMapJoinFastStringCommon(isOuterJoin); + int initialCapacity, float loadFactor, int writeBuffersSize, int maxProbeSize) { + super(mapJoinHashTableFactory, initialCapacity, loadFactor, writeBuffersSize, maxProbeSize); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastTableContainer.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastTableContainer.java deleted file mode 100644 index bd4a595..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastTableContainer.java +++ /dev/null @@ -1,227 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; - -import java.io.IOException; - -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; -import org.apache.hadoop.conf.Configuration; -import org.apache.hadoop.hive.conf.HiveConf; -import org.apache.hadoop.hive.ql.exec.persistence.HashMapWrapper; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinKey; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinObjectSerDeContext; -import org.apache.hadoop.hive.ql.exec.tez.HashTableLoader; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTable; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinTableContainer; -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.ql.plan.MapJoinDesc; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableImplementationType; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKind; -import org.apache.hadoop.hive.serde2.SerDeException; -import org.apache.hadoop.io.BytesWritable; -import org.apache.hadoop.io.Writable; -import org.apache.tez.runtime.library.api.KeyValueReader; - -/** - * HashTableLoader for Tez constructs the hashtable from records read from - * a broadcast edge. - */ -public class VectorMapJoinFastTableContainer implements VectorMapJoinTableContainer { - - private static final Logger LOG = LoggerFactory.getLogger(HashTableLoader.class.getName()); - - private MapJoinDesc desc; - private Configuration hconf; - - private float keyCountAdj; - private int threshold; - private float loadFactor; - private int wbSize; - private long keyCount; - - - private VectorMapJoinFastHashTable VectorMapJoinFastHashTable; - - public VectorMapJoinFastTableContainer(MapJoinDesc desc, Configuration hconf, - long keyCount) throws SerDeException { - - this.desc = desc; - this.hconf = hconf; - - keyCountAdj = HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEKEYCOUNTADJUSTMENT); - threshold = HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLETHRESHOLD); - loadFactor = HiveConf.getFloatVar(hconf, HiveConf.ConfVars.HIVEHASHTABLELOADFACTOR); - wbSize = HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEWBSIZE); - - this.keyCount = keyCount; - - // LOG.info("VectorMapJoinFastTableContainer load keyCountAdj " + keyCountAdj); - // LOG.info("VectorMapJoinFastTableContainer load threshold " + threshold); - // LOG.info("VectorMapJoinFastTableContainer load loadFactor " + loadFactor); - // LOG.info("VectorMapJoinFastTableContainer load wbSize " + wbSize); - - int newThreshold = HashMapWrapper.calculateTableSize( - keyCountAdj, threshold, loadFactor, keyCount); - - // LOG.debug("VectorMapJoinFastTableContainer load newThreshold " + newThreshold); - - VectorMapJoinFastHashTable = createHashTable(newThreshold); - } - - @Override - public VectorMapJoinHashTable vectorMapJoinHashTable() { - return (VectorMapJoinHashTable) VectorMapJoinFastHashTable; - } - - private VectorMapJoinFastHashTable createHashTable(int newThreshold) { - - boolean isOuterJoin = !desc.isNoOuterJoin(); - VectorMapJoinDesc vectorDesc = desc.getVectorDesc(); - HashTableImplementationType hashTableImplementationType = vectorDesc.hashTableImplementationType(); - HashTableKind hashTableKind = vectorDesc.hashTableKind(); - HashTableKeyType hashTableKeyType = vectorDesc.hashTableKeyType(); - boolean minMaxEnabled = vectorDesc.minMaxEnabled(); - - int writeBufferSize = HiveConf.getIntVar(hconf, HiveConf.ConfVars.HIVEHASHTABLEWBSIZE); - - VectorMapJoinFastHashTable hashTable = null; - - switch (hashTableKeyType) { - case BOOLEAN: - case BYTE: - case SHORT: - case INT: - case LONG: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinFastLongHashMap( - minMaxEnabled, isOuterJoin, hashTableKeyType, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinFastLongHashMultiSet( - minMaxEnabled, isOuterJoin, hashTableKeyType, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_SET: - hashTable = new VectorMapJoinFastLongHashSet( - minMaxEnabled, isOuterJoin, hashTableKeyType, - newThreshold, loadFactor, writeBufferSize); - break; - } - break; - - case STRING: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinFastStringHashMap( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinFastStringHashMultiSet( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_SET: - hashTable = new VectorMapJoinFastStringHashSet( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - } - break; - - case MULTI_KEY: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinFastMultiKeyHashMap( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinFastMultiKeyHashMultiSet( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - case HASH_SET: - hashTable = new VectorMapJoinFastMultiKeyHashSet( - isOuterJoin, - newThreshold, loadFactor, writeBufferSize); - break; - } - break; - } - - return hashTable; - } - - @Override - public MapJoinKey putRow(MapJoinObjectSerDeContext keyContext, - Writable currentKey, MapJoinObjectSerDeContext valueContext, - Writable currentValue) throws SerDeException, HiveException, IOException { - - // We are not using the key and value contexts, nor do we support a MapJoinKey. - VectorMapJoinFastHashTable.putRow((BytesWritable) currentKey, (BytesWritable) currentValue); - return null; - } - - @Override - public void seal() { - // Do nothing - } - - @Override - public ReusableGetAdaptor createGetter(MapJoinKey keyTypeFromLoader) { - throw new RuntimeException("Not applicable"); - } - - @Override - public void clear() { - // Do nothing - } - - @Override - public MapJoinKey getAnyKey() { - throw new RuntimeException("Not applicable"); - } - - @Override - public void dumpMetrics() { - // TODO - } - - @Override - public boolean hasSpill() { - return false; - } - - @Override - public int size() { - return VectorMapJoinFastHashTable.size(); - } - - /* - @Override - public com.esotericsoftware.kryo.io.Output getHybridBigTableSpillOutput(int partitionId) { - throw new RuntimeException("Not applicable"); - } - */ -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastValueStore.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastValueStore.java index 570a747..bf985b1 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastValueStore.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/VectorMapJoinFastValueStore.java @@ -20,7 +20,9 @@ import org.slf4j.Logger; import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResultImpl; import org.apache.hadoop.hive.serde2.WriteBuffers; import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; import org.apache.hadoop.hive.serde2.WriteBuffers.Position;; @@ -111,7 +113,8 @@ public WriteBuffers writeBuffers() { return writeBuffers; } - public static class HashMapResult extends VectorMapJoinHashMapResult { + public static class HashMapResult extends MapJoinHashTableResultImpl + implements MapJoinHashMapResult { private VectorMapJoinFastValueStore valueStore; @@ -120,7 +123,6 @@ public WriteBuffers writeBuffers() { private boolean isSingleRow; private int cappedCount; - private boolean haveReadCurrent; private int readIndex; private boolean isEof; @@ -141,7 +143,7 @@ public HashMapResult() { readPos = new Position(); } - public void set(VectorMapJoinFastValueStore valueStore, long valueRefWord) { + public void setMatch(VectorMapJoinFastValueStore valueStore, long valueRefWord) { // LOG.debug("VectorMapJoinFastValueStore set valueRefWord " + Long.toHexString(valueRefWord)); this.valueStore = valueStore; @@ -152,9 +154,9 @@ public void set(VectorMapJoinFastValueStore valueStore, long valueRefWord) { cappedCount = (int) ((valueRefWord & CappedCount.bitMask) >> CappedCount.bitShift); // Position to beginning. - haveReadCurrent = false; readIndex = 0; isEof = false; + mapJoinResult = MapJoinResult.MATCH; } @Override @@ -192,7 +194,6 @@ public ByteSegmentRef first() { } // Position to beginning. - haveReadCurrent = false; readIndex = 0; isEof = false; @@ -330,9 +331,14 @@ public boolean isEof() { } @Override - public void forget() { + public boolean isAliasFilterAvailable() { + return false; } + @Override + public byte aliasFilter() { + return 0; + } @Override public String toString() { diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMap.java deleted file mode 100644 index 512db1b..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMap.java +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface for a single byte array key hash map lookup method. - */ -public interface VectorMapJoinBytesHashMap - extends VectorMapJoinBytesHashTable, VectorMapJoinHashMap { - - /* - * Lookup a byte array key in the hash map. - * - * @param keyBytes - * A byte array containing the key within a range. - * @param keyStart - * The offset the beginning of the key. - * @param keyLength - * The length of the key. - * @param hashMapResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spill (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult lookup(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashMapResult hashMapResult) throws IOException; - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMultiSet.java deleted file mode 100644 index 196403d..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashMultiSet.java +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface for a single byte array key hash multi-set contains method. - */ -public interface VectorMapJoinBytesHashMultiSet - extends VectorMapJoinBytesHashTable, VectorMapJoinHashMultiSet { - - /* - * Lookup an byte array key in the hash multi-set. - * - * @param keyBytes - * A byte array containing the key within a range. - * @param keyStart - * The offset the beginning of the key. - * @param keyLength - * The length of the key. - * @param hashMultiSetResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spilled (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws IOException; - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashSet.java deleted file mode 100644 index a0c93e5..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashSet.java +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface for a single byte array key hash multi-set contains method. - */ -public interface VectorMapJoinBytesHashSet - extends VectorMapJoinBytesHashTable, VectorMapJoinHashSet { - - /* - * Lookup an byte array key in the hash set. - * - * @param keyBytes - * A byte array containing the key within a range. - * @param keyStart - * The offset the beginning of the key. - * @param keyLength - * The length of the key. - * @param hashSetResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spilled (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashSetResult hashSetResult) throws IOException; - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashTable.java deleted file mode 100644 index 7494e1d..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinBytesHashTable.java +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * Interface for a vector map join hash table (which could be a hash map, hash multi-set, or - * hash set) for a single byte array key. - */ -public interface VectorMapJoinBytesHashTable extends VectorMapJoinHashTable { -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMap.java deleted file mode 100644 index 7abe2c8..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMap.java +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * The root interface for a vector map join hash map. - */ -public interface VectorMapJoinHashMap extends VectorMapJoinHashTable { - - /* - * @return A new hash map result implementation specific object. - * - * The object can be used to access the values when there is a match, or - * access spill information when the partition with the key is currently spilled. - */ - VectorMapJoinHashMapResult createHashMapResult(); - -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMapResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMapResult.java deleted file mode 100644 index fa6dedb..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMapResult.java +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; - -/* - * Abstract class for a hash map result. For reading the values, one-by-one. - */ -public abstract class VectorMapJoinHashMapResult extends VectorMapJoinHashTableResult { - - /** - * @return Whether there are any rows (i.e. true for match). - */ - public abstract boolean hasRows(); - - /** - * @return Whether there is 1 value row. - */ - public abstract boolean isSingleRow(); - - /** - * @return Whether there is a capped count available from cappedCount. - */ - public abstract boolean isCappedCountAvailable(); - - /** - * @return The count of values, up to a arbitrary cap limit. When available, the capped - * count can be used to make decisions on how to optimally generate join results. - */ - public abstract int cappedCount(); - - /** - * @return A reference to the first value, or null if there are no values. - */ - public abstract ByteSegmentRef first(); - - /** - * @return The next value, or null if there are no more values to be read. - */ - public abstract ByteSegmentRef next(); - - /** - * @return Whether reading is at the end. - */ - public abstract boolean isEof(); -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSet.java deleted file mode 100644 index 210597d..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSet.java +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -public interface VectorMapJoinHashMultiSet extends VectorMapJoinHashTable { - - /* - * @return A new hash multi-set result implementation specific object. - * - * The object can be used to access the *count* of values when the key is contained in the - * multi-set, or access spill information when the partition with the key is currently spilled. - */ - VectorMapJoinHashMultiSetResult createHashMultiSetResult(); - -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSetResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSetResult.java deleted file mode 100644 index 0728f78..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashMultiSetResult.java +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * Abstract class for a hash multi-set result. - */ -public abstract class VectorMapJoinHashMultiSetResult extends VectorMapJoinHashTableResult { - - protected long count; - - /* - * @return The multi-set count for the lookup key. - */ - public long count() { - return count; - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSet.java deleted file mode 100644 index a26f997..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSet.java +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * The root interface for a vector map join hash set. - */ -public interface VectorMapJoinHashSet extends VectorMapJoinHashTable { - - /* - * @return A new hash set result implementation specific object. - * - * The object can be used to access access spill information when the partition with the key - * is currently spilled. - */ - VectorMapJoinHashSetResult createHashSetResult(); - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSetResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSetResult.java deleted file mode 100644 index 467c4c1..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashSetResult.java +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * Abstract class for a hash set result. - */ -public abstract class VectorMapJoinHashSetResult extends VectorMapJoinHashTableResult { - - // Nothing currently available for hash sets. - -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTable.java deleted file mode 100644 index c7e585c..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTable.java +++ /dev/null @@ -1,47 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.serde2.SerDeException; -import org.apache.hadoop.io.BytesWritable; - -/* - * Root interface for a vector map join hash table (which could be a hash map, hash multi-set, or - * hash set). - */ -public interface VectorMapJoinHashTable { - - - /* - * @param currentKey - * The current key. - * @param currentValue - * The current value. - */ - void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException; - - /** - * Get hash table size - */ - int size(); -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTableResult.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTableResult.java deleted file mode 100644 index ce598e3..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinHashTableResult.java +++ /dev/null @@ -1,81 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * Root abstract class for a hash table result. - */ -public abstract class VectorMapJoinHashTableResult { - - private JoinUtil.JoinResult joinResult; - - private int spillPartitionId; - - public VectorMapJoinHashTableResult() { - joinResult = JoinUtil.JoinResult.NOMATCH; - spillPartitionId = -1; - } - - /** - * @return The join result from the most recent hash map match, or hash multi-set / set contains - * call. - */ - public JoinUtil.JoinResult joinResult() { - return joinResult; - } - - /** - * Set the current join result. - * @param joinResult - * The new join result. - */ - public void setJoinResult(JoinUtil.JoinResult joinResult) { - this.joinResult = joinResult; - } - - /** - * Forget about the most recent hash table lookup or contains call. - */ - public void forget() { - joinResult = JoinUtil.JoinResult.NOMATCH; - } - - /** - * Set the spill partition id. - */ - public void setSpillPartitionId(int spillPartitionId) { - this.spillPartitionId = spillPartitionId; - } - - /** - * @return The Hybrid Grace spill partition id. - */ - public int spillPartitionId() { - return spillPartitionId; - } - - @Override - public String toString() { - StringBuilder sb = new StringBuilder(); - sb.append("joinResult " + joinResult.name()); - return sb.toString(); - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMap.java deleted file mode 100644 index f180d02..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMap.java +++ /dev/null @@ -1,46 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface for a single long key hash map lookup method. - */ -public interface VectorMapJoinLongHashMap - extends VectorMapJoinLongHashTable, VectorMapJoinHashMap { - - /* - * Lookup an long in the hash map. - * - * @param key - * The long key. - * @param hashMapResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spilled (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult lookup(long key, VectorMapJoinHashMapResult hashMapResult) throws IOException; - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMultiSet.java deleted file mode 100644 index 7477584..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashMultiSet.java +++ /dev/null @@ -1,46 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface for a single long key hash multi-set contains method. - */ -public interface VectorMapJoinLongHashMultiSet - extends VectorMapJoinLongHashTable, VectorMapJoinHashMultiSet { - - /* - * Lookup an long in the hash multi-set. - * - * @param key - * The long key. - * @param hashMultiSetResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spilled (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult contains(long key, VectorMapJoinHashMultiSetResult hashMultiSetResult) throws IOException; - -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashSet.java deleted file mode 100644 index 8c28bff..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashSet.java +++ /dev/null @@ -1,46 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; - -/* - * The interface adds the single long key hash multi-set contains method. - */ -public interface VectorMapJoinLongHashSet - extends VectorMapJoinLongHashTable, VectorMapJoinHashSet { - - /* - * Lookup an long in the hash set. - * - * @param key - * The long key. - * @param hashSetResult - * The object to receive small table value(s) information on a MATCH. - * Or, for SPILL, it has information on where to spill the big table row. - * - * @return - * Whether the lookup was a match, no match, or spilled (the partition with the key - * is currently spilled). - */ - JoinUtil.JoinResult contains(long key, VectorMapJoinHashSetResult hashSetResult) throws IOException; - -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashTable.java deleted file mode 100644 index 046a403..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinLongHashTable.java +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -/* - * Interface for a vector map join hash table (which could be a hash map, hash multi-set, or - * hash set) for a single long. - */ -public interface VectorMapJoinLongHashTable extends VectorMapJoinHashTable { - - boolean useMinMax(); - long min(); - long max(); - -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinTableContainer.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinTableContainer.java deleted file mode 100644 index 09631e4..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/hashtable/VectorMapJoinTableContainer.java +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable; - -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; - -public interface VectorMapJoinTableContainer extends MapJoinTableContainer { - - VectorMapJoinHashTable vectorMapJoinHashTable(); - - // com.esotericsoftware.kryo.io.Output getHybridBigTableSpillOutput(int partitionId); -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedCreateHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedCreateHashTable.java deleted file mode 100644 index f34b1cd..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedCreateHashTable.java +++ /dev/null @@ -1,129 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinKey; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.plan.MapJoinDesc; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKind; - -/** - */ -public class VectorMapJoinOptimizedCreateHashTable { - - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOptimizedCreateHashTable.class.getName()); - - public static VectorMapJoinOptimizedHashTable createHashTable(MapJoinDesc desc, - MapJoinTableContainer mapJoinTableContainer) { - - MapJoinKey refKey = mapJoinTableContainer.getAnyKey(); - ReusableGetAdaptor hashMapRowGetter = mapJoinTableContainer.createGetter(refKey); - - boolean isOuterJoin = !desc.isNoOuterJoin(); - VectorMapJoinDesc vectorDesc = desc.getVectorDesc(); - HashTableKind hashTableKind = vectorDesc.hashTableKind(); - HashTableKeyType hashTableKeyType = vectorDesc.hashTableKeyType(); - boolean minMaxEnabled = vectorDesc.minMaxEnabled(); - - VectorMapJoinOptimizedHashTable hashTable = null; - - switch (hashTableKeyType) { - case BOOLEAN: - case BYTE: - case SHORT: - case INT: - case LONG: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinOptimizedLongHashMap( - minMaxEnabled, isOuterJoin, hashTableKeyType, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinOptimizedLongHashMultiSet( - minMaxEnabled, isOuterJoin, hashTableKeyType, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_SET: - hashTable = new VectorMapJoinOptimizedLongHashSet( - minMaxEnabled, isOuterJoin, hashTableKeyType, - mapJoinTableContainer, hashMapRowGetter); - break; - } - break; - - case STRING: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinOptimizedStringHashMap( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinOptimizedStringHashMultiSet( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_SET: - hashTable = new VectorMapJoinOptimizedStringHashSet( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - } - break; - - case MULTI_KEY: - switch (hashTableKind) { - case HASH_MAP: - hashTable = new VectorMapJoinOptimizedMultiKeyHashMap( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_MULTISET: - hashTable = new VectorMapJoinOptimizedMultiKeyHashMultiSet( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - case HASH_SET: - hashTable = new VectorMapJoinOptimizedMultiKeyHashSet( - isOuterJoin, - mapJoinTableContainer, hashMapRowGetter); - break; - } - break; - } - return hashTable; - } - - /* - @Override - public com.esotericsoftware.kryo.io.Output getHybridBigTableSpillOutput(int partitionId) { - - HybridHashTableContainer ht = (HybridHashTableContainer) mapJoinTableContainer; - - HashPartition hp = ht.getHashPartitions()[partitionId]; - - return hp.getMatchfileOutput(); - } - */ -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMap.java deleted file mode 100644 index e56c821..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMap.java +++ /dev/null @@ -1,128 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMap; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; - -public class VectorMapJoinOptimizedHashMap - extends VectorMapJoinOptimizedHashTable - implements VectorMapJoinBytesHashMap { - - @Override - public VectorMapJoinHashMapResult createHashMapResult() { - return new HashMapResult(); - } - - public static class HashMapResult extends VectorMapJoinHashMapResult { - - private BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult; - - public HashMapResult() { - super(); - bytesBytesMultiHashMapResult = new BytesBytesMultiHashMap.Result(); - } - - public BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult() { - return bytesBytesMultiHashMapResult; - } - - @Override - public boolean hasRows() { - return (joinResult() == JoinUtil.JoinResult.MATCH); - } - - @Override - public boolean isSingleRow() { - if (joinResult() != JoinUtil.JoinResult.MATCH) { - throw new RuntimeException("HashMapResult is not a match"); - } - return bytesBytesMultiHashMapResult.isSingleRow(); - } - - @Override - public boolean isCappedCountAvailable() { - return false; - } - - @Override - public int cappedCount() { - return 0; - } - - @Override - public ByteSegmentRef first() { - if (joinResult() != JoinUtil.JoinResult.MATCH) { - throw new RuntimeException("HashMapResult is not a match"); - } - return bytesBytesMultiHashMapResult.first(); - } - - @Override - public ByteSegmentRef next() { - return bytesBytesMultiHashMapResult.next(); - } - - @Override - public boolean isEof() { - return bytesBytesMultiHashMapResult.isEof(); - } - - @Override - public void forget() { - bytesBytesMultiHashMapResult.forget(); - super.forget(); - } - - @Override - public String toString() { - StringBuilder sb = new StringBuilder(); - sb.append("(" + super.toString() + ", "); - sb.append("isSingleRow " + (joinResult() == JoinUtil.JoinResult.MATCH ? isSingleRow() : "") + ")"); - return sb.toString(); - } - } - - @Override - public JoinUtil.JoinResult lookup(byte[] keyBytes, int keyOffset, int keyLength, - VectorMapJoinHashMapResult hashMapResult) throws IOException { - - HashMapResult implementationHashMapResult = (HashMapResult) hashMapResult; - - JoinUtil.JoinResult joinResult = - doLookup(keyBytes, keyOffset, keyLength, - implementationHashMapResult.bytesBytesMultiHashMapResult(), - (VectorMapJoinHashTableResult) hashMapResult); - - return joinResult; - } - - public VectorMapJoinOptimizedHashMap( - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMultiSet.java deleted file mode 100644 index 34de7e1..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashMultiSet.java +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMap; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef; - -public class VectorMapJoinOptimizedHashMultiSet - extends VectorMapJoinOptimizedHashTable - implements VectorMapJoinBytesHashMultiSet { - - @Override - public VectorMapJoinHashMultiSetResult createHashMultiSetResult() { - return new HashMultiSetResult(); - } - - public static class HashMultiSetResult extends VectorMapJoinHashMultiSetResult { - - private BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult; - - private boolean haveCount; - - public HashMultiSetResult() { - super(); - bytesBytesMultiHashMapResult = new BytesBytesMultiHashMap.Result(); - } - - public BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult() { - return bytesBytesMultiHashMapResult; - } - - /* - * @return The multi-set count for the lookup key. - */ - @Override - public long count() { - if (!haveCount) { - if (bytesBytesMultiHashMapResult.isSingleRow()) { - count = 1; - } else { - count = 0; - ByteSegmentRef byteSegmentRef = bytesBytesMultiHashMapResult.first(); - while (byteSegmentRef != null) { - count++; - byteSegmentRef = bytesBytesMultiHashMapResult.next(); - } - } - haveCount = true; - } - return count; - } - - @Override - public void forget() { - haveCount = false; - bytesBytesMultiHashMapResult.forget(); - super.forget(); - } - } - - @Override - public JoinUtil.JoinResult contains(byte[] keyBytes, int keyOffset, int keyLength, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws IOException { - - HashMultiSetResult implementationHashMultiSetResult = (HashMultiSetResult) hashMultiSetResult; - - JoinUtil.JoinResult joinResult = - doLookup(keyBytes, keyOffset, keyLength, - implementationHashMultiSetResult.bytesBytesMultiHashMapResult(), - (VectorMapJoinHashTableResult) hashMultiSetResult); - - return joinResult; - } - - public VectorMapJoinOptimizedHashMultiSet( - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashSet.java deleted file mode 100644 index 93a89d7..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashSet.java +++ /dev/null @@ -1,78 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMap; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; - -public class VectorMapJoinOptimizedHashSet - extends VectorMapJoinOptimizedHashTable - implements VectorMapJoinBytesHashSet { - - @Override - public VectorMapJoinHashSetResult createHashSetResult() { - return new HashSetResult(); - } - - public static class HashSetResult extends VectorMapJoinHashSetResult { - - private BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult; - - public HashSetResult() { - super(); - bytesBytesMultiHashMapResult = new BytesBytesMultiHashMap.Result(); - } - - public BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult() { - return bytesBytesMultiHashMapResult; - } - - @Override - public void forget() { - bytesBytesMultiHashMapResult.forget(); - super.forget(); - } - } - - @Override - public JoinUtil.JoinResult contains(byte[] keyBytes, int keyOffset, int keyLength, - VectorMapJoinHashSetResult hashSetResult) throws IOException { - - HashSetResult implementationHashSetResult = (HashSetResult) hashSetResult; - - JoinUtil.JoinResult joinResult = - doLookup(keyBytes, keyOffset, keyLength, - implementationHashSetResult.bytesBytesMultiHashMapResult(), - (VectorMapJoinHashTableResult) hashSetResult); - - return joinResult; - } - - public VectorMapJoinOptimizedHashSet( - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashTable.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashTable.java deleted file mode 100644 index 5fe7861..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedHashTable.java +++ /dev/null @@ -1,99 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.persistence.BytesBytesMultiHashMap; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainerDirectAccess; -import org.apache.hadoop.hive.ql.exec.persistence.ReusableGetAdaptorDirectAccess; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTable; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult; -import org.apache.hadoop.hive.ql.metadata.HiveException; -import org.apache.hadoop.hive.serde2.SerDeException; -import org.apache.hadoop.io.BytesWritable; -import org.apache.hadoop.io.Writable; - -/* - * Root interface for a vector map join hash table (which could be a hash map, hash multi-set, or - * hash set). - */ -public abstract class VectorMapJoinOptimizedHashTable implements VectorMapJoinHashTable { - - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOptimizedMultiKeyHashMap.class.getName()); - - protected final MapJoinTableContainer originalTableContainer; - protected final MapJoinTableContainerDirectAccess containerDirectAccess; - protected final ReusableGetAdaptorDirectAccess adapatorDirectAccess; - - public static class SerializedBytes { - byte[] bytes; - int offset; - int length; - } - - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - putRowInternal(currentKey, currentValue); - } - - protected void putRowInternal(BytesWritable key, BytesWritable value) - throws SerDeException, HiveException, IOException { - - containerDirectAccess.put((Writable) key, (Writable) value); - } - - public JoinUtil.JoinResult doLookup(byte[] keyBytes, int keyOffset, int keyLength, - BytesBytesMultiHashMap.Result bytesBytesMultiHashMapResult, - VectorMapJoinHashTableResult hashTableResult) { - - hashTableResult.forget(); - - JoinUtil.JoinResult joinResult = - adapatorDirectAccess.setDirect(keyBytes, keyOffset, keyLength, - bytesBytesMultiHashMapResult); - if (joinResult == JoinUtil.JoinResult.SPILL) { - hashTableResult.setSpillPartitionId(adapatorDirectAccess.directSpillPartitionId()); - } - - hashTableResult.setJoinResult(joinResult); - - return joinResult; - } - - public VectorMapJoinOptimizedHashTable( - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - - this.originalTableContainer = originalTableContainer; - containerDirectAccess = (MapJoinTableContainerDirectAccess) originalTableContainer; - adapatorDirectAccess = (ReusableGetAdaptorDirectAccess) hashMapRowGetter; - } - - @Override - public int size() { - return originalTableContainer.size(); - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongCommon.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongCommon.java deleted file mode 100644 index a84de89..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongCommon.java +++ /dev/null @@ -1,171 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedHashTable.SerializedBytes; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; -import org.apache.hadoop.hive.serde2.ByteStream.Output; -import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; - -/* - * An single long value hash map based on the BytesBytesMultiHashMap. - * - * We serialize the long key into BinarySortable format into an output buffer accepted by - * BytesBytesMultiHashMap. - */ -public class VectorMapJoinOptimizedLongCommon { - - private static final Logger LOG = LoggerFactory.getLogger(VectorMapJoinOptimizedLongCommon.class.getName()); - - private boolean isOuterJoin; - - private HashTableKeyType hashTableKeyType; - - // private BinarySortableDeserializeRead keyBinarySortableDeserializeRead; - - private BinarySortableSerializeWrite keyBinarySortableSerializeWrite; - - private transient Output output; - - private transient SerializedBytes serializedBytes; - - // protected boolean useMinMax; - protected long min; - protected long max; - - public boolean useMinMax() { - return false; - } - - public long min() { - return min; - } - - public long max() { - return max; - } - - /* - * For now, just use MapJoinBytesTableContainer / HybridHashTableContainer directly. - - public void adaptPutRow(VectorMapJoinOptimizedHashTable hashTable, - BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - if (useMinMax) { - // Peek at the BinarySortable key to extract the long so we can determine min and max. - byte[] keyBytes = currentKey.getBytes(); - int keyLength = currentKey.getLength(); - keyBinarySortableDeserializeRead.set(keyBytes, 0, keyLength); - if (keyBinarySortableDeserializeRead.readCheckNull()) { - if (isOuterJoin) { - return; - } else { - // For inner join, we expect all NULL values to have been filtered out before now. - throw new HiveException("Unexpected NULL"); - } - } - long key = 0; - switch (hashTableKeyType) { - case BOOLEAN: - key = (keyBinarySortableDeserializeRead.readBoolean() ? 1 : 0); - break; - case BYTE: - key = (long) keyBinarySortableDeserializeRead.readByte(); - break; - case SHORT: - key = (long) keyBinarySortableDeserializeRead.readShort(); - break; - case INT: - key = (long) keyBinarySortableDeserializeRead.readInt(); - break; - case LONG: - key = keyBinarySortableDeserializeRead.readLong(); - break; - default: - throw new RuntimeException("Unexpected hash table key type " + hashTableKeyType.name()); - } - if (key < min) { - min = key; - } - if (key > max) { - max = key; - } - - // byte[] bytes = Arrays.copyOf(currentKey.get(), currentKey.getLength()); - // LOG.debug("VectorMapJoinOptimizedLongCommon adaptPutRow key " + key + " min " + min + " max " + max + " hashTableKeyType " + hashTableKeyType.name() + " hex " + Hex.encodeHexString(bytes)); - - } - - hashTable.putRowInternal(currentKey, currentValue); - } - */ - - public SerializedBytes serialize(long key) throws IOException { - keyBinarySortableSerializeWrite.reset(); - - switch (hashTableKeyType) { - case BOOLEAN: - keyBinarySortableSerializeWrite.writeBoolean(key == 1); - break; - case BYTE: - keyBinarySortableSerializeWrite.writeByte((byte) key); - break; - case SHORT: - keyBinarySortableSerializeWrite.writeShort((short) key); - break; - case INT: - keyBinarySortableSerializeWrite.writeInt((int) key); - break; - case LONG: - keyBinarySortableSerializeWrite.writeLong(key); - break; - default: - throw new RuntimeException("Unexpected hash table key type " + hashTableKeyType.name()); - } - - // byte[] bytes = Arrays.copyOf(output.getData(), output.getLength()); - // LOG.debug("VectorMapJoinOptimizedLongCommon serialize key " + key + " hashTableKeyType " + hashTableKeyType.name() + " hex " + Hex.encodeHexString(bytes)); - - serializedBytes.bytes = output.getData(); - serializedBytes.offset = 0; - serializedBytes.length = output.getLength(); - - return serializedBytes; - } - - public VectorMapJoinOptimizedLongCommon( - boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType) { - this.isOuterJoin = isOuterJoin; - // useMinMax = minMaxEnabled; - min = Long.MAX_VALUE; - max = Long.MIN_VALUE; - this.hashTableKeyType = hashTableKeyType; - // PrimitiveTypeInfo[] primitiveTypeInfos = { TypeInfoFactory.longTypeInfo }; - // keyBinarySortableDeserializeRead = new BinarySortableDeserializeRead(primitiveTypeInfos); - keyBinarySortableSerializeWrite = new BinarySortableSerializeWrite(1); - output = new Output(); - keyBinarySortableSerializeWrite.set(output); - serializedBytes = new SerializedBytes(); - } -} diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMap.java deleted file mode 100644 index 403d265..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMap.java +++ /dev/null @@ -1,82 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMap; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; - -/* - * An single long value hash map based on the BytesBytesMultiHashMap. - * - * We serialize the long key into BinarySortable format into an output buffer accepted by - * BytesBytesMultiHashMap. - */ -public class VectorMapJoinOptimizedLongHashMap - extends VectorMapJoinOptimizedHashMap - implements VectorMapJoinLongHashMap { - - private VectorMapJoinOptimizedLongCommon longCommon; - - @Override - public boolean useMinMax() { - return longCommon.useMinMax(); - } - - @Override - public long min() { - return longCommon.min(); - } - - @Override - public long max() { - return longCommon.max(); - } - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - longCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult lookup(long key, - VectorMapJoinHashMapResult hashMapResult) throws IOException { - - SerializedBytes serializedBytes = longCommon.serialize(key); - - return super.lookup(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashMapResult); - } - - public VectorMapJoinOptimizedLongHashMap( - boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - longCommon = new VectorMapJoinOptimizedLongCommon(minMaxEnabled, isOuterJoin, hashTableKeyType); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMultiSet.java deleted file mode 100644 index 5fb8c3a..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashMultiSet.java +++ /dev/null @@ -1,83 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashMultiSet; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; - -/* - * An single long value hash map based on the BytesBytesMultiHashMultiSet. - * - * We serialize the long key into BinarySortable format into an output buffer accepted by - * BytesBytesMultiHashMultiSet. - */ -public class VectorMapJoinOptimizedLongHashMultiSet - extends VectorMapJoinOptimizedHashMultiSet - implements VectorMapJoinLongHashMultiSet { - - private VectorMapJoinOptimizedLongCommon longCommon; - - @Override - public boolean useMinMax() { - return longCommon.useMinMax(); - } - - @Override - public long min() { - return longCommon.min(); - } - - @Override - public long max() { - return longCommon.max(); - } - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - longCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult contains(long key, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws IOException { - - SerializedBytes serializedBytes = longCommon.serialize(key); - - return super.contains(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashMultiSetResult); - - } - - public VectorMapJoinOptimizedLongHashMultiSet( - boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - longCommon = new VectorMapJoinOptimizedLongCommon(minMaxEnabled, isOuterJoin, hashTableKeyType); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashSet.java deleted file mode 100644 index c41505a..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedLongHashSet.java +++ /dev/null @@ -1,83 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinLongHashSet; -import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; - -/* - * An single long value hash map based on the BytesBytesMultiHashSet. - * - * We serialize the long key into BinarySortable format into an output buffer accepted by - * BytesBytesMultiHashSet. - */ -public class VectorMapJoinOptimizedLongHashSet - extends VectorMapJoinOptimizedHashSet - implements VectorMapJoinLongHashSet { - - private VectorMapJoinOptimizedLongCommon longCommon; - - @Override - public boolean useMinMax() { - return longCommon.useMinMax(); - } - - @Override - public long min() { - return longCommon.min(); - } - - @Override - public long max() { - return longCommon.max(); - } - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - longCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult contains(long key, - VectorMapJoinHashSetResult hashSetResult) throws IOException { - - SerializedBytes serializedBytes = longCommon.serialize(key); - - return super.contains(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashSetResult); - - } - - public VectorMapJoinOptimizedLongHashSet( - boolean minMaxEnabled, boolean isOuterJoin, HashTableKeyType hashTableKeyType, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - longCommon = new VectorMapJoinOptimizedLongCommon(minMaxEnabled, isOuterJoin, hashTableKeyType); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMap.java deleted file mode 100644 index 4f3e20e..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMap.java +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; - -/* - * An multi-key hash map based on the BytesBytesMultiHashMap. - */ -public class VectorMapJoinOptimizedMultiKeyHashMap - extends VectorMapJoinOptimizedHashMap { - - // UNDONE: How to look for all NULLs in a multi-key????? Let nulls through for now. - - public VectorMapJoinOptimizedMultiKeyHashMap(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMultiSet.java deleted file mode 100644 index b95a2dd..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashMultiSet.java +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; - -/* - * An multi-key hash map based on the BytesBytesMultiHashMultiSet. - */ -public class VectorMapJoinOptimizedMultiKeyHashMultiSet - extends VectorMapJoinOptimizedHashMultiSet { - - // UNDONE: How to look for all NULLs in a multi-key????? Let nulls through for now. - - public VectorMapJoinOptimizedMultiKeyHashMultiSet(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashSet.java deleted file mode 100644 index 35ecc2a..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedMultiKeyHashSet.java +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; - -/* - * An multi-key hash map based on the BytesBytesMultiHashSet. - */ -public class VectorMapJoinOptimizedMultiKeyHashSet - extends VectorMapJoinOptimizedHashSet { - - // UNDONE: How to look for all NULLs in a multi-key????? Let nulls through for now. - - public VectorMapJoinOptimizedMultiKeyHashSet(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringCommon.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringCommon.java deleted file mode 100644 index 39c2d49..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringCommon.java +++ /dev/null @@ -1,98 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedHashTable.SerializedBytes; -import org.apache.hadoop.hive.serde2.ByteStream.Output; -import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; - -/* - * An single byte array value hash map based on the BytesBytesMultiHashMap. - * - * Since BytesBytesMultiHashMap does not interpret the key as BinarySortable we optimize - * this case and just reference the byte array key directly for the lookup instead of serializing - * the byte array into BinarySortable. We rely on it just doing byte array equality comparisons. - */ -public class VectorMapJoinOptimizedStringCommon { - - // private boolean isOuterJoin; - - // private BinarySortableDeserializeRead keyBinarySortableDeserializeRead; - - // private ReadStringResults readStringResults; - - private BinarySortableSerializeWrite keyBinarySortableSerializeWrite; - - private transient Output output; - - private transient SerializedBytes serializedBytes; - - /* - private BytesWritable bytesWritable; - - public void adaptPutRow(VectorMapJoinOptimizedHashTable hashTable, - BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - byte[] keyBytes = currentKey.getBytes(); - int keyLength = currentKey.getLength(); - keyBinarySortableDeserializeRead.set(keyBytes, 0, keyLength); - if (keyBinarySortableDeserializeRead.readCheckNull()) { - if (isOuterJoin) { - return; - } else { - // For inner join, we expect all NULL values to have been filtered out before now. - throw new HiveException("Unexpected NULL"); - } - } - keyBinarySortableDeserializeRead.readString(readStringResults); - - bytesWritable.set(readStringResults.bytes, readStringResults.start, readStringResults.length); - - hashTable.putRowInternal(bytesWritable, currentValue); - } - */ - - public SerializedBytes serialize(byte[] keyBytes, int keyStart, int keyLength) throws IOException { - - keyBinarySortableSerializeWrite.reset(); - keyBinarySortableSerializeWrite.writeString(keyBytes, keyStart, keyLength); - - serializedBytes.bytes = output.getData(); - serializedBytes.offset = 0; - serializedBytes.length = output.getLength(); - - return serializedBytes; - - } - - public VectorMapJoinOptimizedStringCommon(boolean isOuterJoin) { - // this.isOuterJoin = isOuterJoin; - // PrimitiveTypeInfo[] primitiveTypeInfos = { TypeInfoFactory.stringTypeInfo }; - // keyBinarySortableDeserializeRead = new BinarySortableDeserializeRead(primitiveTypeInfos); - // readStringResults = keyBinarySortableDeserializeRead.createReadStringResults(); - // bytesWritable = new BytesWritable(); - keyBinarySortableSerializeWrite = new BinarySortableSerializeWrite(1); - output = new Output(); - keyBinarySortableSerializeWrite.set(output); - serializedBytes = new SerializedBytes(); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMap.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMap.java deleted file mode 100644 index 220c05e..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMap.java +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMap; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; - -/* - * An multi-key hash map based on the BytesBytesMultiHashMap. - */ -public class VectorMapJoinOptimizedStringHashMap - extends VectorMapJoinOptimizedHashMap - implements VectorMapJoinBytesHashMap { - - private VectorMapJoinOptimizedStringCommon stringCommon; - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - stringCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult lookup(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashMapResult hashMapResult) throws IOException { - - SerializedBytes serializedBytes = stringCommon.serialize(keyBytes, keyStart, keyLength); - - return super.lookup(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashMapResult); - - } - - public VectorMapJoinOptimizedStringHashMap(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - stringCommon = new VectorMapJoinOptimizedStringCommon(isOuterJoin); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMultiSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMultiSet.java deleted file mode 100644 index b6c6958..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashMultiSet.java +++ /dev/null @@ -1,64 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMultiSetResult; - -/* - * An multi-key hash map based on the BytesBytesMultiHashMultiSet. - */ -public class VectorMapJoinOptimizedStringHashMultiSet - extends VectorMapJoinOptimizedHashMultiSet - implements VectorMapJoinBytesHashMultiSet { - - private VectorMapJoinOptimizedStringCommon stringCommon; - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - stringCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashMultiSetResult hashMultiSetResult) throws IOException { - - SerializedBytes serializedBytes = stringCommon.serialize(keyBytes, keyStart, keyLength); - - return super.contains(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashMultiSetResult); - - - } - - public VectorMapJoinOptimizedStringHashMultiSet(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - stringCommon = new VectorMapJoinOptimizedStringCommon(isOuterJoin); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashSet.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashSet.java deleted file mode 100644 index f921b9c..0000000 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/optimized/VectorMapJoinOptimizedStringHashSet.java +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized; - -import java.io.IOException; - -import org.apache.hadoop.hive.ql.exec.JoinUtil.JoinResult; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer.ReusableGetAdaptor; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashSetResult; - -/* - * An multi-key hash map based on the BytesBytesMultiHashSet. - */ -public class VectorMapJoinOptimizedStringHashSet - extends VectorMapJoinOptimizedHashSet - implements VectorMapJoinBytesHashSet { - - private VectorMapJoinOptimizedStringCommon stringCommon; - - /* - @Override - public void putRow(BytesWritable currentKey, BytesWritable currentValue) - throws SerDeException, HiveException, IOException { - - stringCommon.adaptPutRow((VectorMapJoinOptimizedHashTable) this, currentKey, currentValue); - } - */ - - @Override - public JoinResult contains(byte[] keyBytes, int keyStart, int keyLength, - VectorMapJoinHashSetResult hashSetResult) throws IOException { - - SerializedBytes serializedBytes = stringCommon.serialize(keyBytes, keyStart, keyLength); - - return super.contains(serializedBytes.bytes, serializedBytes.offset, serializedBytes.length, - hashSetResult); - - } - - public VectorMapJoinOptimizedStringHashSet(boolean isOuterJoin, - MapJoinTableContainer originalTableContainer, ReusableGetAdaptor hashMapRowGetter) { - super(originalTableContainer, hashMapRowGetter); - stringCommon = new VectorMapJoinOptimizedStringCommon(isOuterJoin); - } -} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkCommonOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkCommonOperator.java index a79a649..75f7dd0 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkCommonOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkCommonOperator.java @@ -32,9 +32,10 @@ import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContextRegion; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; -import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesSerialized; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesFast; import org.apache.hadoop.hive.ql.io.HiveKey; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.BaseWork; @@ -65,6 +66,32 @@ private static final String CLASS_NAME = VectorReduceSinkCommonOperator.class.getName(); private static final Log LOG = LogFactory.getLog(CLASS_NAME); + protected abstract String getLoggingPrefix(); + + // For debug tracing: information about the map or reduce task, operator, operator class, etc. + protected transient String loggingPrefix; + + protected String getLoggingPrefix(String className) { + if (loggingPrefix == null) { + initLoggingPrefix(className); + } + return loggingPrefix; + } + + protected void initLoggingPrefix(String className) { + if (hconf == null) { + // Constructor time... + loggingPrefix = className; + } else { + // Determine the name of our map or reduce task for debug tracing. + BaseWork work = Utilities.getMapWork(hconf); + if (work == null) { + work = Utilities.getReduceWork(hconf); + } + loggingPrefix = className + " " + work.getName() + " " + getOperatorId(); + } + } + protected VectorReduceSinkDesc vectorDesc; /** @@ -82,6 +109,7 @@ // This is map of which vectorized row batch columns are the key columns. // And, their types. protected int[] reduceSinkKeyColumnMap; + protected String[] reduceSinkKeyColumnNames; protected TypeInfo[] reduceSinkKeyTypeInfos; // Optional vectorized key expressions that need to be run on each batch. @@ -90,6 +118,7 @@ // This is map of which vectorized row batch columns are the value columns. // And, their types. protected int[] reduceSinkValueColumnMap; + protected String[] reduceSinkValueColumnNames; protected TypeInfo[] reduceSinkValueTypeInfos; // Optional vectorized value expressions that need to be run on each batch. @@ -99,6 +128,8 @@ // transient. //--------------------------------------------------------------------------- + protected transient Configuration hconf; + // Whether there is to be a tag added to the end of each key and the tag value. private transient boolean reduceSkipTag; private transient byte reduceTagByte; @@ -128,7 +159,7 @@ private transient OutputCollector out; // The object that determines equal key series. - protected transient VectorKeySeriesSerialized serializedKeySeries; + protected transient VectorKeySeriesFast serializedKeySeriesFast; private transient long numRows = 0; private transient long cntr = 1; @@ -160,12 +191,26 @@ public VectorReduceSinkCommonOperator(VectorizationContext vContext, OperatorDes // Since a key expression can be a calculation and the key will go into a scratch column, // we need the mapping and type information. reduceSinkKeyColumnMap = vectorReduceSinkInfo.getReduceSinkKeyColumnMap(); + reduceSinkKeyColumnNames = vectorReduceSinkInfo.getReduceSinkKeyColumnNames(); reduceSinkKeyTypeInfos = vectorReduceSinkInfo.getReduceSinkKeyTypeInfos(); reduceSinkKeyExpressions = vectorReduceSinkInfo.getReduceSinkKeyExpressions(); reduceSinkValueColumnMap = vectorReduceSinkInfo.getReduceSinkValueColumnMap(); + reduceSinkValueColumnNames = vectorReduceSinkInfo.getReduceSinkValueColumnNames(); reduceSinkValueTypeInfos = vectorReduceSinkInfo.getReduceSinkValueTypeInfos(); reduceSinkValueExpressions = vectorReduceSinkInfo.getReduceSinkValueExpressions(); + + if (isLogDebugEnabled) { + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkKeyColumnMap " + Arrays.toString(reduceSinkKeyColumnMap)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkKeyColumnNames " + Arrays.toString(reduceSinkKeyColumnNames)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkKeyTypeInfos " + Arrays.toString(reduceSinkKeyTypeInfos)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkKeyExpressions " + Arrays.toString(reduceSinkKeyExpressions)); + + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkValueColumnMap " + Arrays.toString(reduceSinkValueColumnMap)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkValueColumnNames " + Arrays.toString(reduceSinkValueColumnNames)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkValueTypeInfos " + Arrays.toString(reduceSinkValueTypeInfos)); + LOG.debug(getLoggingPrefix() + " VectorReduceSinkCommonOperator constructor reduceSinkValueExpressions " + Arrays.toString(reduceSinkValueExpressions)); + } } // Get the sort order @@ -184,6 +229,7 @@ public VectorReduceSinkCommonOperator(VectorizationContext vContext, OperatorDes @Override protected void initializeOp(Configuration hconf) throws HiveException { + this.hconf = hconf; super.initializeOp(hconf); if (LOG.isDebugEnabled()) { @@ -229,7 +275,8 @@ protected void initializeOp(Configuration hconf) throws HiveException { throw new HiveException(e); } - valueLazyBinarySerializeWrite = new LazyBinarySerializeWrite(reduceSinkValueColumnMap.length); + valueLazyBinarySerializeWrite = + new LazyBinarySerializeWrite(reduceSinkValueColumnMap.length); valueVectorSerializeRow = new VectorSerializeRow( @@ -256,7 +303,7 @@ public void process(Object row, int tag) throws HiveException { if (batch.size == 0) { if (LOG.isDebugEnabled()) { - LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty"); + LOG.debug(getLoggingPrefix() + " batch #" + batchCounter + " empty"); } return; } @@ -275,7 +322,7 @@ public void process(Object row, int tag) throws HiveException { } } - serializedKeySeries.processBatch(batch); + serializedKeySeriesFast.processBatch(batch); boolean selectedInUse = batch.selectedInUse; int[] selected = batch.selected; @@ -285,7 +332,7 @@ public void process(Object row, int tag) throws HiveException { int end; int batchIndex; do { - if (serializedKeySeries.getCurrentIsAllNull()) { + if (serializedKeySeriesFast.getCurrentIsAllNull()) { // Use the same logic as ReduceSinkOperator.toHiveKey. // @@ -302,24 +349,24 @@ public void process(Object row, int tag) throws HiveException { } else { // One serialized key for 1 or more rows for the duplicate keys. - // LOG.info("reduceSkipTag " + reduceSkipTag + " tag " + tag + " reduceTagByte " + (int) reduceTagByte + " keyLength " + serializedKeySeries.getSerializedLength()); - // LOG.info("process offset " + serializedKeySeries.getSerializedStart() + " length " + serializedKeySeries.getSerializedLength()); - keyLength = serializedKeySeries.getSerializedLength(); + // LOG.info(getLoggingPrefix() + " reduceSkipTag " + reduceSkipTag + " tag " + tag + " reduceTagByte " + (int) reduceTagByte + " keyLength " + serializedKeySeriesFast.getSerializedLength()); + // LOG.info(getLoggingPrefix() + " process offset " + serializedKeySeriesFast.getSerializedStart() + " length " + serializedKeySeriesFast.getSerializedLength()); + keyLength = serializedKeySeriesFast.getSerializedLength(); if (tag == -1 || reduceSkipTag) { - keyWritable.set(serializedKeySeries.getSerializedBytes(), - serializedKeySeries.getSerializedStart(), keyLength); + keyWritable.set(serializedKeySeriesFast.getSerializedBytes(), + serializedKeySeriesFast.getSerializedStart(), keyLength); } else { keyWritable.setSize(keyLength + 1); - System.arraycopy(serializedKeySeries.getSerializedBytes(), - serializedKeySeries.getSerializedStart(), keyWritable.get(), 0, keyLength); + System.arraycopy(serializedKeySeriesFast.getSerializedBytes(), + serializedKeySeriesFast.getSerializedStart(), keyWritable.get(), 0, keyLength); keyWritable.get()[keyLength] = reduceTagByte; } keyWritable.setDistKeyLength(keyLength); - keyWritable.setHashCode(serializedKeySeries.getCurrentHashCode()); + keyWritable.setHashCode(serializedKeySeriesFast.getCurrentHashCode()); } - logical = serializedKeySeries.getCurrentLogical(); - end = logical + serializedKeySeries.getCurrentDuplicateCount(); + logical = serializedKeySeriesFast.getCurrentLogical(); + end = logical + serializedKeySeriesFast.getCurrentDuplicateCount(); do { batchIndex = (selectedInUse ? selected[logical] : logical); @@ -331,7 +378,7 @@ public void process(Object row, int tag) throws HiveException { collect(keyWritable, valueBytesWritable); } while (++logical < end); - if (!serializedKeySeries.next()) { + if (!serializedKeySeriesFast.next()) { break; } } while (true); @@ -358,7 +405,7 @@ protected void collect(BytesWritable keyWritable, Writable valueWritable) throws } // BytesWritable valueBytesWritable = (BytesWritable) valueWritable; - // LOG.info("VectorReduceSinkCommonOperator collect keyWritable " + keyWritable.getLength() + " " + + // LOG.info(getLoggingPrefix() + " collect keyWritable " + keyWritable.getLength() + " " + // VectorizedBatchUtil.displayBytes(keyWritable.getBytes(), 0, keyWritable.getLength()) + // " valueWritable " + valueBytesWritable.getLength() + // VectorizedBatchUtil.displayBytes(valueBytesWritable.getBytes(), 0, valueBytesWritable.getLength())); diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkLongOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkLongOperator.java index cec5660..0aa2a8c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkLongOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkLongOperator.java @@ -22,7 +22,7 @@ import org.apache.commons.logging.LogFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; -import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLongSerialized; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesLongFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; @@ -37,6 +37,10 @@ private static final String CLASS_NAME = VectorReduceSinkLongOperator.class.getName(); private static final Log LOG = LogFactory.getLog(CLASS_NAME); + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + // The column number and type information for this one column long reduce key. private transient int singleKeyColumn; private transient PrimitiveTypeInfo singleKeyColumnPrimitiveTypeInfo; @@ -65,8 +69,8 @@ protected void initializeOp(Configuration hconf) throws HiveException { singleKeyColumn = reduceSinkKeyColumnMap[0]; singleKeyColumnPrimitiveTypeInfo = (PrimitiveTypeInfo) reduceSinkKeyTypeInfos[0]; - serializedKeySeries = - new VectorKeySeriesLongSerialized( + serializedKeySeriesFast = + new VectorKeySeriesLongFast( singleKeyColumn, singleKeyColumnPrimitiveTypeInfo, keyBinarySortableSerializeWrite); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkMultiKeyOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkMultiKeyOperator.java index a4ef66b..fa777c1 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkMultiKeyOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkMultiKeyOperator.java @@ -22,7 +22,7 @@ import org.apache.commons.logging.LogFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; -import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesMultiSerialized; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesMultiFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; @@ -37,6 +37,10 @@ private static final String CLASS_NAME = VectorReduceSinkMultiKeyOperator.class.getName(); private static final Log LOG = LogFactory.getLog(CLASS_NAME); + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + // The above members are initialized by the constructor and must not be // transient. //--------------------------------------------------------------------------- @@ -58,11 +62,11 @@ public VectorReduceSinkMultiKeyOperator(VectorizationContext vContext, OperatorD protected void initializeOp(Configuration hconf) throws HiveException { super.initializeOp(hconf); - VectorKeySeriesMultiSerialized serializedMultiKeySeries = - new VectorKeySeriesMultiSerialized( + VectorKeySeriesMultiFast serializedMultiKeySeries = + new VectorKeySeriesMultiFast( keyBinarySortableSerializeWrite); serializedMultiKeySeries.init(reduceSinkKeyTypeInfos, reduceSinkKeyColumnMap); - serializedKeySeries = serializedMultiKeySeries; + serializedKeySeriesFast = serializedMultiKeySeries; } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkStringOperator.java ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkStringOperator.java index b6cb527..12049fc 100644 --- ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkStringOperator.java +++ ql/src/java/org/apache/hadoop/hive/ql/exec/vector/reducesink/VectorReduceSinkStringOperator.java @@ -22,11 +22,10 @@ import org.apache.commons.logging.LogFactory; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext; -import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesBytesSerialized; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.fast.VectorKeySeriesBytesFast; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.plan.OperatorDesc; import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite; -import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; /* * Specialized class for native vectorized reduce sink that is reducing on a single long key column. @@ -37,6 +36,10 @@ private static final String CLASS_NAME = VectorReduceSinkStringOperator.class.getName(); private static final Log LOG = LogFactory.getLog(CLASS_NAME); + protected String getLoggingPrefix() { + return super.getLoggingPrefix(CLASS_NAME); + } + // The column number and type information for this one column string reduce key. private transient int singleKeyColumn; @@ -63,8 +66,8 @@ protected void initializeOp(Configuration hconf) throws HiveException { singleKeyColumn = reduceSinkKeyColumnMap[0]; - serializedKeySeries = - new VectorKeySeriesBytesSerialized( + serializedKeySeriesFast = + new VectorKeySeriesBytesFast( singleKeyColumn, keyBinarySortableSerializeWrite); } } \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/optimizer/physical/Vectorizer.java ql/src/java/org/apache/hadoop/hive/ql/optimizer/physical/Vectorizer.java index a842649..d0dd8d2 100644 --- ql/src/java/org/apache/hadoop/hive/ql/optimizer/physical/Vectorizer.java +++ ql/src/java/org/apache/hadoop/hive/ql/optimizer/physical/Vectorizer.java @@ -41,7 +41,6 @@ import org.apache.hadoop.hive.metastore.api.hive_metastoreConstants; import org.apache.hadoop.hive.ql.exec.*; import org.apache.hadoop.hive.ql.exec.mr.MapRedTask; -import org.apache.hadoop.hive.ql.exec.persistence.MapJoinKey; import org.apache.hadoop.hive.ql.exec.spark.SparkTask; import org.apache.hadoop.hive.ql.exec.tez.TezTask; import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor; @@ -61,6 +60,7 @@ import org.apache.hadoop.hive.ql.exec.vector.reducesink.VectorReduceSinkMultiKeyOperator; import org.apache.hadoop.hive.ql.exec.vector.reducesink.VectorReduceSinkStringOperator; import org.apache.hadoop.hive.ql.exec.vector.ColumnVector.Type; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; import org.apache.hadoop.hive.ql.exec.vector.VectorMapJoinOperator; import org.apache.hadoop.hive.ql.exec.vector.VectorMapJoinOuterFilteredOperator; import org.apache.hadoop.hive.ql.exec.vector.VectorSMBMapJoinOperator; @@ -70,6 +70,15 @@ import org.apache.hadoop.hive.ql.exec.vector.expressions.IdentityExpression; import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; import org.apache.hadoop.hive.ql.exec.vector.expressions.aggregates.VectorAggregateExpression; +import org.apache.hadoop.hive.ql.exec.vector.VectorExpressionDescriptor.Mode; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByGlobalCountStarOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByGlobalOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByLongCountStarOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByLongOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByMergePartialCountStarOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByMergePartialOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByMultiKeyFixedLenOperator; +import org.apache.hadoop.hive.ql.exec.vector.groupby.VectorGroupByStringOperator; import org.apache.hadoop.hive.ql.io.AcidUtils; import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatchCtx; import org.apache.hadoop.hive.ql.lib.DefaultGraphWalker; @@ -98,6 +107,7 @@ import org.apache.hadoop.hive.ql.plan.MapJoinDesc; import org.apache.hadoop.hive.ql.plan.MapWork; import org.apache.hadoop.hive.ql.plan.OperatorDesc; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.ProcessingMode; import org.apache.hadoop.hive.ql.plan.VectorPartitionConversion; import org.apache.hadoop.hive.ql.plan.PartitionDesc; import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc; @@ -108,11 +118,16 @@ import org.apache.hadoop.hive.ql.plan.TableDesc; import org.apache.hadoop.hive.ql.plan.TableScanDesc; import org.apache.hadoop.hive.ql.plan.TezWork; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.ColumnAggregationInfo; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunction; +import org.apache.hadoop.hive.ql.plan.VectorGroupByInfo.AggregationFunctionInfo; import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableImplementationType; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKind; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinInfo; import org.apache.hadoop.hive.ql.plan.VectorReduceSinkDesc; import org.apache.hadoop.hive.ql.plan.VectorReduceSinkInfo; import org.apache.hadoop.hive.ql.plan.VectorPartitionDesc; @@ -165,8 +180,10 @@ import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.StructTypeInfo; import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory; import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils; +import com.google.common.base.Preconditions; import com.google.common.base.Joiner; public class Vectorizer implements PhysicalPlanResolver { @@ -1610,15 +1627,15 @@ private boolean validateAggregationIsPrimitive(VectorAggregateExpression vectorA return (outputObjInspector.getCategory() == ObjectInspector.Category.PRIMITIVE); } - private boolean validateAggregationDesc(AggregationDesc aggDesc, boolean isReduceMergePartial, + private boolean validateAggregationDesc(AggregationDesc aggrDesc, boolean isReduceMergePartial, boolean hasKeys) { - String udfName = aggDesc.getGenericUDAFName().toLowerCase(); + String udfName = aggrDesc.getGenericUDAFName().toLowerCase(); if (!supportedAggregationUdfs.contains(udfName)) { LOG.info("Cannot vectorize groupby aggregate expression: UDF " + udfName + " not supported"); return false; } - if (aggDesc.getParameters() != null && !validateExprNodeDesc(aggDesc.getParameters())) { + if (aggrDesc.getParameters() != null && !validateExprNodeDesc(aggrDesc.getParameters())) { LOG.info("Cannot vectorize groupby aggregate expression: UDF parameters not supported"); return false; } @@ -1627,7 +1644,7 @@ private boolean validateAggregationDesc(AggregationDesc aggDesc, boolean isReduc VectorizationContext vc = new ValidatorVectorizationContext(); VectorAggregateExpression vectorAggrExpr; try { - vectorAggrExpr = vc.getAggregatorExpression(aggDesc, isReduceMergePartial); + vectorAggrExpr = vc.getAggregatorExpression(aggrDesc, isReduceMergePartial); } catch (Exception e) { // We should have already attempted to vectorize in validateAggregationDesc. LOG.info("Vectorization of aggreation should have succeeded ", e); @@ -1716,7 +1733,8 @@ private boolean isBigTableOnlyResults(MapJoinDesc desc) { } Operator specializeMapJoinOperator(Operator op, - VectorizationContext vContext, MapJoinDesc desc) throws HiveException { + VectorizationContext vContext, MapJoinDesc desc, VectorMapJoinInfo vectorMapJoinInfo) + throws HiveException { Operator vectorOp = null; Class> opClass = null; @@ -1724,14 +1742,7 @@ private boolean isBigTableOnlyResults(MapJoinDesc desc) { VectorMapJoinDesc.HashTableKind hashTableKind = HashTableKind.NONE; VectorMapJoinDesc.HashTableKeyType hashTableKeyType = HashTableKeyType.NONE; - if (HiveConf.getBoolVar(hiveConf, - HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_FAST_HASHTABLE_ENABLED)) { - hashTableImplementationType = HashTableImplementationType.FAST; - } else { - // Restrict to using BytesBytesMultiHashMap via MapJoinBytesTableContainer or - // HybridHashTableContainer. - hashTableImplementationType = HashTableImplementationType.OPTIMIZED; - } + hashTableImplementationType = HashTableImplementationType.FAST; int joinType = desc.getConds()[0].getType(); @@ -1751,20 +1762,31 @@ private boolean isBigTableOnlyResults(MapJoinDesc desc) { Map> keyExprs = desc.getKeys(); List bigTableKeyExprs = keyExprs.get(posBigTable); if (bigTableKeyExprs.size() == 1) { - String typeName = bigTableKeyExprs.get(0).getTypeString(); - LOG.info("Vectorizer vectorizeOperator map join typeName " + typeName); - if (typeName.equals("boolean")) { + TypeInfo typeInfo = bigTableKeyExprs.get(0).getTypeInfo(); + LOG.info("Vectorizer vectorizeOperator map join typeName " + typeInfo.getTypeName()); + switch (((PrimitiveTypeInfo) typeInfo).getPrimitiveCategory()) { + case BOOLEAN: hashTableKeyType = HashTableKeyType.BOOLEAN; - } else if (typeName.equals("tinyint")) { + break; + case BYTE: hashTableKeyType = HashTableKeyType.BYTE; - } else if (typeName.equals("smallint")) { + break; + case SHORT: hashTableKeyType = HashTableKeyType.SHORT; - } else if (typeName.equals("int")) { + break; + case INT: hashTableKeyType = HashTableKeyType.INT; - } else if (typeName.equals("bigint") || typeName.equals("long")) { + break; + case LONG: hashTableKeyType = HashTableKeyType.LONG; - } else if (VectorizationContext.isStringFamily(typeName)) { + break; + case STRING: + case CHAR: + case VARCHAR: + case BINARY: hashTableKeyType = HashTableKeyType.STRING; + default: + // Stay with multi-key. } } } @@ -1855,11 +1877,10 @@ private boolean isBigTableOnlyResults(MapJoinDesc desc) { throw new HiveException("Unknown join type " + joinType); } break; + default: + throw new RuntimeException("Unexpected hash table key type " + hashTableKeyType.name()); } - vectorOp = OperatorFactory.getVectorOperator(opClass, op.getConf(), vContext); - LOG.info("Vectorizer vectorizeOperator map join class " + vectorOp.getClass().getSimpleName()); - boolean minMaxEnabled = HiveConf.getBoolVar(hiveConf, HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_MINMAX_ENABLED); @@ -1868,6 +1889,12 @@ private boolean isBigTableOnlyResults(MapJoinDesc desc) { vectorDesc.setHashTableKind(hashTableKind); vectorDesc.setHashTableKeyType(hashTableKeyType); vectorDesc.setMinMaxEnabled(minMaxEnabled); + vectorDesc.setVectorMapJoinInfo(vectorMapJoinInfo); + + + vectorOp = OperatorFactory.getVectorOperator(opClass, op.getConf(), vContext); + LOG.info("Vectorizer vectorizeOperator map join class " + vectorOp.getClass().getSimpleName()); + return vectorOp; } @@ -1882,53 +1909,504 @@ private boolean onExpressionHasNullSafes(MapJoinDesc desc) { } private boolean canSpecializeMapJoin(Operator op, MapJoinDesc desc, - boolean isTez) { - - boolean specialize = false; + boolean isTez, VectorizationContext vContext, VectorMapJoinInfo vectorMapJoinInfo) + throws HiveException { - if (op instanceof MapJoinOperator && - HiveConf.getBoolVar(hiveConf, + if (!(op instanceof MapJoinOperator) || + !HiveConf.getBoolVar(hiveConf, HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_ENABLED)) { + return false; + } + + // Currently, only under Tez and non-N-way joins. + if (!isTez || desc.getConds().length != 1 || onExpressionHasNullSafes(desc)) { + return false; + } + + byte posBigTable = (byte) desc.getPosBigTable(); + + List keyDesc = desc.getKeys().get(posBigTable); + VectorExpression[] allBigTableKeyExpressions = vContext.getVectorExpressions(keyDesc); + + // Since a key expression can be a calculation and the key will go into a scratch column, + // we need the mapping and type information. + int[] bigTableKeyColumnMap = new int[allBigTableKeyExpressions.length]; + String[] bigTableKeyColumnNames = new String[allBigTableKeyExpressions.length]; + TypeInfo[] bigTableKeyTypeInfos = new TypeInfo[allBigTableKeyExpressions.length]; + ArrayList bigTableKeyExpressionsList = new ArrayList(); + VectorExpression[] bigTableKeyExpressions; + for (int i = 0; i < bigTableKeyColumnMap.length; i++) { + VectorExpression ve = allBigTableKeyExpressions[i]; + if (!IdentityExpression.isColumnOnly(ve)) { + bigTableKeyExpressionsList.add(ve); + } + bigTableKeyColumnMap[i] = ve.getOutputColumn(); + + ExprNodeDesc exprNode = keyDesc.get(i); + bigTableKeyColumnNames[i] = exprNode.toString(); + bigTableKeyTypeInfos[i] = exprNode.getTypeInfo(); + } + if (bigTableKeyExpressionsList.size() == 0) { + bigTableKeyExpressions = null; + } else { + bigTableKeyExpressions = bigTableKeyExpressionsList.toArray(new VectorExpression[0]); + } + + List bigTableExprs = desc.getExprs().get(posBigTable); + VectorExpression[] allBigTableValueExpressions = vContext.getVectorExpressions(bigTableExprs); + + /* + * Similarly, we need a mapping since a value expression can be a calculation and the value + * will go into a scratch column. + */ + int[] bigTableValueColumnMap = new int[allBigTableValueExpressions.length]; + String[] bigTableValueColumnNames = new String[allBigTableValueExpressions.length]; + TypeInfo[] bigTableValueTypeInfos = new TypeInfo[allBigTableValueExpressions.length]; + ArrayList bigTableValueExpressionsList = new ArrayList(); + VectorExpression[] bigTableValueExpressions; + for (int i = 0; i < bigTableValueColumnMap.length; i++) { + VectorExpression ve = allBigTableValueExpressions[i]; + if (!IdentityExpression.isColumnOnly(ve)) { + bigTableValueExpressionsList.add(ve); + } + bigTableValueColumnMap[i] = ve.getOutputColumn(); + + ExprNodeDesc exprNode = bigTableExprs.get(i); + bigTableValueColumnNames[i] = exprNode.toString(); + bigTableValueTypeInfos[i] = exprNode.getTypeInfo(); + } + if (bigTableValueExpressionsList.size() == 0) { + bigTableValueExpressions = null; + } else { + bigTableValueExpressions = bigTableValueExpressionsList.toArray(new VectorExpression[0]); + } + + vectorMapJoinInfo.setBigTableKeyColumnMap(bigTableKeyColumnMap); + vectorMapJoinInfo.setBigTableKeyColumnNames(bigTableKeyColumnNames); + vectorMapJoinInfo.setBigTableKeyTypeInfos(bigTableKeyTypeInfos); + vectorMapJoinInfo.setBigTableKeyExpressions(bigTableKeyExpressions); + + vectorMapJoinInfo.setBigTableValueColumnMap(bigTableValueColumnMap); + vectorMapJoinInfo.setBigTableValueColumnNames(bigTableValueColumnNames); + vectorMapJoinInfo.setBigTableValueTypeInfos(bigTableValueTypeInfos); + vectorMapJoinInfo.setBigTableValueExpressions(bigTableValueExpressions); + + return true; + } - // Currently, only under Tez and non-N-way joins. - if (isTez && desc.getConds().length == 1 && !onExpressionHasNullSafes(desc)) { + private Operator specializeGroupByOperator( + Operator op, VectorizationContext vContext, GroupByDesc desc, + VectorGroupByInfo vectorGroupByInfo) throws HiveException { - // Ok, all basic restrictions satisfied so far... - specialize = true; + Operator vectorOp = null; + Class> opClass = null; - if (!HiveConf.getBoolVar(hiveConf, - HiveConf.ConfVars.HIVE_VECTORIZATION_MAPJOIN_NATIVE_FAST_HASHTABLE_ENABLED)) { + boolean isOnlyCountStar = vectorGroupByInfo.getHasCountStar() && !vectorGroupByInfo.getHasNonCountStar(); - // We are using the optimized hash table we have further - // restrictions (using optimized and key type). + VectorGroupByDesc.HashTableKeyType hashTableKeyType = vectorGroupByInfo.getHashTableKeyType(); - if (!HiveConf.getBoolVar(hiveConf, - HiveConf.ConfVars.HIVEMAPJOINUSEOPTIMIZEDTABLE)) { - specialize = false; + ProcessingMode processingMode = vectorGroupByInfo.getProcessingMode(); + switch (processingMode) { + case GLOBAL: + if (isOnlyCountStar) { + opClass = VectorGroupByGlobalCountStarOperator.class; + } else { + opClass = VectorGroupByGlobalOperator.class; + } + break; + case HASH: + if (isOnlyCountStar && hashTableKeyType == VectorGroupByDesc.HashTableKeyType.LONG) { + opClass = VectorGroupByLongCountStarOperator.class; + } else { + boolean keysAreAllFixedLength = vectorGroupByInfo.getKeysAreAllFixedLength(); + switch (hashTableKeyType) { + case LONG: + opClass = VectorGroupByLongOperator.class; + break; + case STRING: + opClass = VectorGroupByStringOperator.class; + break; + case MULTI_KEY: + if (keysAreAllFixedLength) { + opClass = VectorGroupByMultiKeyFixedLenOperator.class; } else { - byte posBigTable = (byte) desc.getPosBigTable(); - Map> keyExprs = desc.getKeys(); - List bigTableKeyExprs = keyExprs.get(posBigTable); - for (ExprNodeDesc exprNodeDesc : bigTableKeyExprs) { - String typeName = exprNodeDesc.getTypeString(); - if (!MapJoinKey.isSupportedField(typeName)) { - specialize = false; - break; - } - } + // UNDONE... + throw new HiveException("Multiple variable length keys not supported yet"); } + break; + default: + throw new HiveException("Unknown hash table key type " + hashTableKeyType); + } + } + break; + case MERGE_PARTIAL: + if (isOnlyCountStar) { + opClass = VectorGroupByMergePartialCountStarOperator.class; + } else { + opClass = VectorGroupByMergePartialOperator.class; + } + break; + default: + throw new RuntimeException("Unexpected vector group by processing mode " + + processingMode.name()); + } + + VectorGroupByDesc vectorDesc = desc.getVectorDesc(); + + vectorDesc.setHashTableKeyType(hashTableKeyType); + vectorDesc.setAggregationInfo(vectorGroupByInfo); + + vectorDesc.setVectorOutput(true); + + vectorOp = OperatorFactory.getVectorOperator(opClass, op.getConf(), vContext); + LOG.info("Vectorizer vectorizeOperator group by class " + vectorOp.getClass().getSimpleName()); + LOG.info("Vectorizer vectorizeOperator group by key types " + Arrays.toString(vectorGroupByInfo.getGroupByKeyTypeInfos())); + return vectorOp; + } + + // + // Currently, only these aggregations are supported: + // COUNT(*) or COUNT(column), or + // MIN, MAX, SUM supported for Hive integer data types (i.e. LongColumnVector). + // + private boolean canSpecializeAggregationFunction(AggregationDesc aggrDesc, int funcNum, + int inputColumnNum, TypeInfo inputTypeInfo, Type inputColumnVectorType, + AggregationFunctionInfo aggrFuncInfo) + throws HiveException { + + String aggregationFunctionName = aggrDesc.getGenericUDAFName().toLowerCase(); + AggregationFunction aggregationFunction = + AggregationFunction.nameToAggregationFunction.get(aggregationFunctionName); + if (aggregationFunction == null) { + return false; + } + + // UNDONE: Not ready for AVG yet. + if (aggregationFunction == AggregationFunction.AVG_FUNC) { + return false; + } + + if (inputColumnNum == -1) { + if (aggregationFunction != AggregationFunction.COUNT_FUNC) { + return false; + } + + // COUNT(*) has no associated column information. + + TypeInfo countStarTypeInfo = TypeInfoFactory.longTypeInfo; + aggrFuncInfo.set(aggregationFunction, funcNum, countStarTypeInfo); + return true; + } + + // UNDONE: For now, restrict input ColumnVector types to LONG and DOUBLE. + if (inputColumnVectorType != ColumnVector.Type.LONG && + inputColumnVectorType != ColumnVector.Type.DOUBLE) { + return false; + } + + TypeInfo outputTypeInfo; + switch (aggregationFunction) { + case MIN_FUNC: + case MAX_FUNC: + // Same as input type. + outputTypeInfo = inputTypeInfo; + break; + case COUNT_FUNC: + outputTypeInfo = TypeInfoFactory.longTypeInfo; + break; + case SUM_FUNC: + switch (inputColumnVectorType) { + case LONG: + // Largest integer. + outputTypeInfo = TypeInfoFactory.longTypeInfo; + break; + case DOUBLE: + // Float/double can handle larger values -- so keep input type. + outputTypeInfo = inputTypeInfo; + break; + default: + return false; + } + break; + default: + throw new RuntimeException("Unexpected aggregation function " + aggregationFunction.name()); + } + aggrFuncInfo.set(aggregationFunction, funcNum, outputTypeInfo); + + return true; + } + + private boolean canSpecializeGroupBy(GroupByDesc desc, + boolean isTez, VectorizationContext vContext, + VectorGroupByInfo vectorGroupByInfo) throws HiveException { + + if (!HiveConf.getBoolVar(hiveConf, + HiveConf.ConfVars.HIVE_VECTORIZATION_GROUPBY_NATIVE_ENABLED) || + true) { + return false; + } + + VectorGroupByDesc vectorDesc = desc.getVectorDesc(); + + List keysDescs = desc.getKeys(); + + int outputKeyLength = keysDescs.size(); + + ProcessingMode processingMode; + if (outputKeyLength == 0) { + // Hash and MergePartial global aggregation are both handled here. + processingMode = ProcessingMode.GLOBAL; + } else if (vectorDesc.isReduceMergePartial()) { + // Sorted GroupBy of vector batches where an individual batch has the same group key (e.g. reduce). + processingMode = ProcessingMode.MERGE_PARTIAL; + } else if (vectorDesc.isReduceStreaming()) { + processingMode = ProcessingMode.UNSORTED_STREAMING; + + // UNDONE: Currently, no support for this mode. + } else { + processingMode = ProcessingMode.HASH; + } + + // We are doing work here we'd normally do in VectorGroupByCommonOperator's constructor. + // So if we later decide not to specialize, we'll just waste any scratch columns allocated... + + VectorExpression[] allKeyExpressions = vContext.getVectorExpressions(keysDescs); + + // Since a key expression can be a calculation and the key will go into a scratch column, + // we need the mapping and type information. + int[] groupByKeyColumnMap = new int[allKeyExpressions.length]; + TypeInfo[] groupByKeyTypeInfos = new TypeInfo[allKeyExpressions.length]; + Type[] groupByKeyColumnVectorTypes = new Type[allKeyExpressions.length]; + ArrayList groupByKeyExpressionsList = new ArrayList(); + VectorExpression[] groupByKeyExpressions; + ColumnVector.Type columnVectorType; + boolean keysAreAllFixedLength = true; // Assume. + for (int i = 0; i < groupByKeyColumnMap.length; i++) { + VectorExpression ve = allKeyExpressions[i]; + groupByKeyColumnMap[i] = ve.getOutputColumn(); + groupByKeyTypeInfos[i] = keysDescs.get(i).getTypeInfo(); + columnVectorType = + VectorizationContext.getColumnVectorTypeFromTypeInfo(groupByKeyTypeInfos[i]); + + switch (columnVectorType) { + case LONG: + case DOUBLE: + // This key is fixed length. + break; + case BYTES: + keysAreAllFixedLength = false; + break; + case DECIMAL: + // UNDONE: For the moment we do not support DECIMAL type. + return false; + default: + throw new RuntimeException("Unexpected column vector type " + columnVectorType.name()); + } + + groupByKeyColumnVectorTypes[i] = columnVectorType; + if (!IdentityExpression.isColumnOnly(ve)) { + groupByKeyExpressionsList.add(ve); + } + } + if (groupByKeyExpressionsList.size() == 0) { + groupByKeyExpressions = null; + } else { + groupByKeyExpressions = groupByKeyExpressionsList.toArray(new VectorExpression[0]); + } + + // By default, we can always use the multi-key class. + VectorGroupByDesc.HashTableKeyType hashTableKeyType = VectorGroupByDesc.HashTableKeyType.MULTI_KEY; + + // Look for single column specialization. + if (groupByKeyColumnVectorTypes.length == 1) { + switch (groupByKeyColumnVectorTypes[0]) { + case LONG: + hashTableKeyType = VectorGroupByDesc.HashTableKeyType.LONG; + break; + case BYTES: + hashTableKeyType = VectorGroupByDesc.HashTableKeyType.STRING; + default: + // Stay with multi-key. + break; + } + } + + if (hashTableKeyType == VectorGroupByDesc.HashTableKeyType.MULTI_KEY && + !keysAreAllFixedLength) { + // UNDONE: Multiple variable length keys not supported yet. + return false; + } + + ArrayList aggrDescs = desc.getAggregators(); + + int[] aggregationFunctionInputColumns = new int[aggrDescs.size()]; + TypeInfo[] aggregationFunctionInputTypeInfos = new TypeInfo[aggrDescs.size()]; + Type[] aggregationFunctionInputColumnVectorTypes = new Type[aggrDescs.size()]; + ArrayList aggregationFunctionInputExpressionsList = new ArrayList(); + VectorExpression[] aggregationFunctionInputExpressions; + for (int i = 0; i < aggrDescs.size(); ++i) { + AggregationDesc aggDesc = aggrDescs.get(i); + ArrayList paramDescList = aggDesc.getParameters(); + int size = paramDescList.size(); + if (size == 0) { + // No input column for COUNT(*) + aggregationFunctionInputColumns[i] = -1; + aggregationFunctionInputColumnVectorTypes[i] = Type.NONE; + } else if (size == 1) { + ExprNodeDesc exprDesc = paramDescList.get(0); + VectorExpression ve = vContext.getVectorExpression(exprDesc, Mode.PROJECTION); + aggregationFunctionInputColumns[i] = ve.getOutputColumn(); + aggregationFunctionInputTypeInfos[i] = exprDesc.getTypeInfo(); + aggregationFunctionInputColumnVectorTypes[i] = + VectorizationContext.getColumnVectorTypeFromTypeInfo(aggregationFunctionInputTypeInfos[i]); + if (!IdentityExpression.isColumnOnly(ve)) { + aggregationFunctionInputExpressionsList.add(ve); + } + } else { + return false; + } + } + if (aggregationFunctionInputExpressionsList.size() == 0) { + aggregationFunctionInputExpressions = null; + } else { + aggregationFunctionInputExpressions = groupByKeyExpressionsList.toArray(new VectorExpression[0]); + } + + HashMap columnAggregationMap = + new HashMap(); + + /** + * First, add the keys to the map. + */ + for (int keyNum = 0; keyNum < groupByKeyColumnMap.length; keyNum++) { + int inputColumnNum = groupByKeyColumnMap[keyNum]; + columnVectorType = groupByKeyColumnVectorTypes[keyNum]; + ColumnAggregationInfo columnAggrInfo = + ColumnAggregationInfo.createKey(keyNum, inputColumnNum, columnVectorType); + columnAggregationMap.put(inputColumnNum, columnAggrInfo); + } + + /** + * Now process the aggregation functions, add any new non-key columns to the map. + */ + ArrayList aggrFuncInfoList = new ArrayList(); + int logicalNonKeyColumnNum = 0; + for (int funcNum = 0; funcNum < aggregationFunctionInputColumns.length; funcNum++) { + int inputColumnNum = aggregationFunctionInputColumns[funcNum]; + TypeInfo inputTypeInfo = aggregationFunctionInputTypeInfos[funcNum]; + Type inputColumnVectorType = aggregationFunctionInputColumnVectorTypes[funcNum]; + AggregationDesc aggrDesc = aggrDescs.get(funcNum); + AggregationFunctionInfo aggrFuncInfo = new AggregationFunctionInfo(); + + if (!canSpecializeAggregationFunction(aggrDesc, funcNum, inputColumnNum, + inputTypeInfo, inputColumnVectorType, aggrFuncInfo)) { + LOG.info("canSpecializeGroupBy could not specialize function " + funcNum); + return false; + } + + if (inputColumnNum == -1) { + // COUNT(*) + Preconditions.checkState(aggrFuncInfo.getAggregationFunction() == AggregationFunction.COUNT_FUNC); + + // No associated column information. + aggrFuncInfo.setColumnAggregationInfo(null); + } else { + ColumnAggregationInfo columnAggrInfo; + if (columnAggregationMap.containsKey(inputColumnNum)) { + columnAggrInfo = columnAggregationMap.get(inputColumnNum); } else { + columnAggrInfo = + ColumnAggregationInfo.createNonKey(inputColumnNum, logicalNonKeyColumnNum++, + inputColumnVectorType); + columnAggregationMap.put(inputColumnNum, columnAggrInfo); + } + + // Indicate an aggregation is being used for a column. + columnAggrInfo.addFunction(aggrFuncInfo.getAggregationFunction()); + aggrFuncInfo.setColumnAggregationInfo(columnAggrInfo); + } + + aggrFuncInfoList.add(aggrFuncInfo); + } + + /** + * What is the overall COUNT(*) picture? + */ + boolean hasNonCountStar = false; + boolean hasCountStar = false; + TypeInfo[] aggregationFunctionOutputTypeInfos = new TypeInfo[aggrDescs.size()]; + for (int i = 0; i < aggrFuncInfoList.size(); i++) { + AggregationFunctionInfo aggrFuncInfo = aggrFuncInfoList.get(i); - // With the fast hash table implementation, we currently do not support - // Hybrid Grace Hash Join. + aggregationFunctionOutputTypeInfos[i] = aggrFuncInfo.getOutputTypeInfo(); - if (desc.isHybridHashJoin()) { - specialize = false; + ColumnAggregationInfo columnAggrInfo = aggrFuncInfo.getColumnAggregationInfo(); + if (columnAggrInfo == null) { + // COUNT(*) + hasCountStar = true; + continue; + } + + // Other aggregation -- can't specialize to COUNT(*) only. + hasNonCountStar = true; + + boolean[] aggregationFunctionsPresent = columnAggrInfo.getAggregationFunctionsPresent(); + if (columnAggrInfo.getIsKey()) { + + // Aggregation on a key has trivial cases that can be computed at hash table flush time. + + for (AggregationFunction aggrFunc : AggregationFunction.values()) { + if (aggregationFunctionsPresent[aggrFunc.getValue()]) { + switch (aggrFunc) { + case MIN_FUNC: + case MAX_FUNC: + // Trivial copy key when key non-NULL. + break; + case COUNT_FUNC: + // Use COUNT(*) count when key non-NULL. + hasCountStar = true; + break; + case SUM_FUNC: + // Use COUNT(*) multiplied by key when key non-NULL. + hasCountStar = true; + break; + default: + throw new RuntimeException("Aggregation function " + aggrFunc.name() + " not expected"); + } } } + } else { + + // Non-Key column. + } } - return specialize; + + vectorGroupByInfo.setProcessingMode(processingMode); + + vectorGroupByInfo.setKeysAreAllFixedLength(keysAreAllFixedLength); + + vectorGroupByInfo.setGroupByKeyColumnMap(groupByKeyColumnMap); + vectorGroupByInfo.setGroupByKeyTypeInfos(groupByKeyTypeInfos); + vectorGroupByInfo.setGroupByKeyColumnVectorTypes(groupByKeyColumnVectorTypes); + vectorGroupByInfo.setGroupByKeyExpressions(groupByKeyExpressions); + + vectorGroupByInfo.setHashTableKeyType(hashTableKeyType); + + vectorGroupByInfo.setAggregationFunctionInputColumns(aggregationFunctionInputColumns); + vectorGroupByInfo.setAggregationFunctionInputTypeInfos(aggregationFunctionInputTypeInfos); + vectorGroupByInfo.setAggregationFunctionInputColumnVectorTypes(aggregationFunctionInputColumnVectorTypes); + vectorGroupByInfo.setAggregationFunctionInputExpressions(aggregationFunctionInputExpressions); + + vectorGroupByInfo.setAggregationFunctionOutputTypeInfos(aggregationFunctionOutputTypeInfos); + + AggregationFunctionInfo[] aggrFuncInfos = aggrFuncInfoList.toArray(new AggregationFunctionInfo[0]); + vectorGroupByInfo.setAggregationFunctionInfos(aggrFuncInfos); + vectorGroupByInfo.setColumnAggregationMap(columnAggregationMap); + vectorGroupByInfo.setHasNonCountStar(hasNonCountStar); + vectorGroupByInfo.setHasCountStar(hasCountStar); + + return true; } private Operator specializeReduceSinkOperator( @@ -2004,7 +2482,8 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, VectorReduceSinkInfo vectorReduceSinkInfo) throws HiveException { if (!HiveConf.getBoolVar(hiveConf, - HiveConf.ConfVars.HIVE_VECTORIZATION_REDUCESINK_NEW_ENABLED)) { + HiveConf.ConfVars.HIVE_VECTORIZATION_REDUCESINK_NEW_ENABLED) || + true) { return false; } @@ -2057,6 +2536,7 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, // Since a key expression can be a calculation and the key will go into a scratch column, // we need the mapping and type information. int[] reduceSinkKeyColumnMap = new int[allKeyExpressions.length]; + String[] reduceSinkKeyColumnNames = new String[allKeyExpressions.length]; TypeInfo[] reduceSinkKeyTypeInfos = new TypeInfo[allKeyExpressions.length]; Type[] reduceSinkKeyColumnVectorTypes = new Type[allKeyExpressions.length]; ArrayList groupByKeyExpressionsList = new ArrayList(); @@ -2064,12 +2544,15 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, for (int i = 0; i < reduceSinkKeyColumnMap.length; i++) { VectorExpression ve = allKeyExpressions[i]; reduceSinkKeyColumnMap[i] = ve.getOutputColumn(); - reduceSinkKeyTypeInfos[i] = keysDescs.get(i).getTypeInfo(); - reduceSinkKeyColumnVectorTypes[i] = - VectorizationContext.getColumnVectorTypeFromTypeInfo(reduceSinkKeyTypeInfos[i]); if (!IdentityExpression.isColumnOnly(ve)) { groupByKeyExpressionsList.add(ve); } + + ExprNodeDesc exprNode = keysDescs.get(i); + reduceSinkKeyColumnNames[i] = exprNode.toString(); + reduceSinkKeyTypeInfos[i] = exprNode.getTypeInfo(); + reduceSinkKeyColumnVectorTypes[i] = + VectorizationContext.getColumnVectorTypeFromTypeInfo(reduceSinkKeyTypeInfos[i]); } if (groupByKeyExpressionsList.size() == 0) { reduceSinkKeyExpressions = null; @@ -2080,20 +2563,24 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, ArrayList valueDescs = desc.getValueCols(); VectorExpression[] allValueExpressions = vContext.getVectorExpressions(valueDescs); - int[] reduceSinkValueColumnMap = new int[valueDescs.size()]; - TypeInfo[] reduceSinkValueTypeInfos = new TypeInfo[valueDescs.size()]; - Type[] reduceSinkValueColumnVectorTypes = new Type[valueDescs.size()]; + int[] reduceSinkValueColumnMap = new int[allValueExpressions.length]; + String[] reduceSinkValueColumnNames = new String[allValueExpressions.length]; + TypeInfo[] reduceSinkValueTypeInfos = new TypeInfo[allValueExpressions.length]; + Type[] reduceSinkValueColumnVectorTypes = new Type[allValueExpressions.length]; ArrayList reduceSinkValueExpressionsList = new ArrayList(); VectorExpression[] reduceSinkValueExpressions; for (int i = 0; i < valueDescs.size(); ++i) { VectorExpression ve = allValueExpressions[i]; reduceSinkValueColumnMap[i] = ve.getOutputColumn(); - reduceSinkValueTypeInfos[i] = valueDescs.get(i).getTypeInfo(); - reduceSinkValueColumnVectorTypes[i] = - VectorizationContext.getColumnVectorTypeFromTypeInfo(reduceSinkValueTypeInfos[i]); if (!IdentityExpression.isColumnOnly(ve)) { reduceSinkValueExpressionsList.add(ve); } + + ExprNodeDesc exprNode = valueDescs.get(i); + reduceSinkValueColumnNames[i] = exprNode.toString(); + reduceSinkValueTypeInfos[i] = exprNode.getTypeInfo(); + reduceSinkValueColumnVectorTypes[i] = + VectorizationContext.getColumnVectorTypeFromTypeInfo(reduceSinkValueTypeInfos[i]); } if (reduceSinkValueExpressionsList.size() == 0) { reduceSinkValueExpressions = null; @@ -2102,11 +2589,13 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, } vectorReduceSinkInfo.setReduceSinkKeyColumnMap(reduceSinkKeyColumnMap); + vectorReduceSinkInfo.setReduceSinkKeyColumnNames(reduceSinkKeyColumnNames); vectorReduceSinkInfo.setReduceSinkKeyTypeInfos(reduceSinkKeyTypeInfos); vectorReduceSinkInfo.setReduceSinkKeyColumnVectorTypes(reduceSinkKeyColumnVectorTypes); vectorReduceSinkInfo.setReduceSinkKeyExpressions(reduceSinkKeyExpressions); vectorReduceSinkInfo.setReduceSinkValueColumnMap(reduceSinkValueColumnMap); + vectorReduceSinkInfo.setReduceSinkValueColumnNames(reduceSinkValueColumnNames); vectorReduceSinkInfo.setReduceSinkValueTypeInfos(reduceSinkValueTypeInfos); vectorReduceSinkInfo.setReduceSinkValueColumnVectorTypes(reduceSinkValueColumnVectorTypes); vectorReduceSinkInfo.setReduceSinkValueExpressions(reduceSinkValueExpressions); @@ -2121,8 +2610,9 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, switch (op.getType()) { case MAPJOIN: { + VectorMapJoinInfo vectorMapJoinInfo = new VectorMapJoinInfo(); MapJoinDesc desc = (MapJoinDesc) op.getConf(); - boolean specialize = canSpecializeMapJoin(op, desc, isTez || isSpark); + boolean specialize = canSpecializeMapJoin(op, desc, isTez || isSpark, vContext, vectorMapJoinInfo); if (!specialize) { @@ -2146,15 +2636,27 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, } else { - // TEMPORARY Until Native Vector Map Join with Hybrid passes tests... - // HiveConf.setBoolVar(physicalContext.getConf(), - // HiveConf.ConfVars.HIVEUSEHYBRIDGRACEHASHJOIN, false); + vectorOp = specializeMapJoinOperator(op, vContext, desc, vectorMapJoinInfo); + } + } + break; + case GROUPBY: + { + VectorGroupByInfo vectorGroupByInfo = new VectorGroupByInfo(); + GroupByDesc desc = (GroupByDesc) op.getConf(); + boolean specialize = canSpecializeGroupBy(desc, isTez, vContext, vectorGroupByInfo); + + if (!specialize) { + + vectorOp = OperatorFactory.getVectorOperator(op.getConf(), vContext); + + } else { + + vectorOp = specializeGroupByOperator(op, vContext, desc, vectorGroupByInfo); - vectorOp = specializeMapJoinOperator(op, vContext, desc); } } break; - case REDUCESINK: { VectorReduceSinkInfo vectorReduceSinkInfo = new VectorReduceSinkInfo(); @@ -2172,7 +2674,6 @@ private boolean canSpecializeReduceSink(ReduceSinkDesc desc, } } break; - case GROUPBY: case FILTER: case SELECT: case FILESINK: diff --git ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByDesc.java ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByDesc.java index e613a4e..e365a98 100644 --- ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByDesc.java +++ ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByDesc.java @@ -18,6 +18,8 @@ package org.apache.hadoop.hive.ql.plan; +import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; + /** * VectorGroupByDesc. * @@ -30,15 +32,28 @@ private static long serialVersionUID = 1L; + public static enum HashTableKeyType { + NONE, + LONG, + STRING, + MULTI_KEY + } + private boolean isReduceMergePartial; private boolean isVectorOutput; private boolean isReduceStreaming; + private HashTableKeyType hashTableKeyType; + + private VectorGroupByInfo vectorGroupByInfo; + public VectorGroupByDesc() { - this.isReduceMergePartial = false; - this.isVectorOutput = false; + isReduceMergePartial = false; + isVectorOutput = false; + hashTableKeyType = HashTableKeyType.NONE; + vectorGroupByInfo = null; } public boolean isReduceMergePartial() { @@ -64,4 +79,20 @@ public void setIsReduceStreaming(boolean isReduceStreaming) { public boolean isReduceStreaming() { return isReduceStreaming; } + + public HashTableKeyType hashTableKeyType() { + return hashTableKeyType; + } + + public void setHashTableKeyType(HashTableKeyType hashTableKeyType) { + this.hashTableKeyType = hashTableKeyType; + } + + public void setAggregationInfo(VectorGroupByInfo vectorGroupByInfo) { + this.vectorGroupByInfo = vectorGroupByInfo; + } + + public VectorGroupByInfo getVectorGroupByInfo() { + return vectorGroupByInfo; + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByInfo.java ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByInfo.java new file mode 100644 index 0000000..b0ce804 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/plan/VectorGroupByInfo.java @@ -0,0 +1,418 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.plan; + +import java.util.Arrays; +import java.util.HashMap; +import java.util.Map; + +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.ColumnVector.Type; +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.ql.plan.VectorGroupByDesc.HashTableKeyType; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; + +import com.google.common.base.Preconditions; + +/** + * VectorGroupByAggregrationInfo. + * + * A convenience data structure that has information needed to vectorize group by. + * + * It is created by the Vectorizer when it is determining whether it can specialize so the + * information doesn't have to be recreated again and agains by the VectorGroupByOperator's + * constructors and later during execution. + */ +public class VectorGroupByInfo { + + private static long serialVersionUID = 1L; + + public enum ProcessingMode { + GLOBAL, + HASH, + MERGE_PARTIAL, + UNSORTED_STREAMING + } + + // The aggregation functions we currently support. + public enum AggregationFunction { + MIN_FUNC (0, "min", false), + MAX_FUNC (1, "max", false), + COUNT_FUNC (2, "count", false), + SUM_FUNC (3, "sum", false), + AVG_FUNC (4, "avg", true); + + private final int value; + private final String name; + private final boolean isComplex; + + public static final AggregationFunction[] aggregationFunctionValues = + AggregationFunction.values(); + + AggregationFunction(int val, String name, boolean isComplex) { + this.value = val; + this.name = name; + this.isComplex = isComplex; + } + + public int getValue() { + return value; + } + public boolean getIsComplex() { + return isComplex; + } + + public static final Map nameToAggregationFunction = + new HashMap(); + static { + for (AggregationFunction aggrFunc : values()) { + nameToAggregationFunction.put(aggrFunc.name, aggrFunc); + } + } + } + + /** + * Information on the aggregation(s) being done on a particular column. + * + * In vectorized group by, we can do one or more aggregations on a column in one pass during + * execution. + */ + public static class ColumnAggregationInfo { + + // Aggregation on a key can be trivial or easy, so we remember this information. + private final boolean isKey; + + private final int inputColumnNum; + + private final int logicalNum; + + private final ColumnVector.Type inputColumnVectorType; + + // Indicates which aggregation function(s) are being applied to this column. + private boolean[] aggregationFunctionsPresent; + + private ColumnAggregationInfo(boolean isKey, int inputColumnNum, int logicalNum, + ColumnVector.Type inputColumnVectorType) { + this.isKey = isKey; + this.inputColumnNum = inputColumnNum; + this.logicalNum = logicalNum; + Preconditions.checkState(inputColumnVectorType != ColumnVector.Type.NONE); + this.inputColumnVectorType = inputColumnVectorType; + + aggregationFunctionsPresent = new boolean[AggregationFunction.aggregationFunctionValues.length]; + } + + public static ColumnAggregationInfo createKey(int keyNum, int inputColumnNum, + ColumnVector.Type inputColumnVectorType) { + return new ColumnAggregationInfo(/* isKey */ true, inputColumnNum, + /* logicalNum */ keyNum, inputColumnVectorType); + } + + public static ColumnAggregationInfo createNonKey(int inputColumnNum, int logicalNonKeyColumnNum, + ColumnVector.Type inputColumnVectorType) { + return new ColumnAggregationInfo(/* isKey */ false, inputColumnNum, + /* logicalNum */ logicalNonKeyColumnNum, inputColumnVectorType); + } + + public boolean getIsKey() { + return isKey; + } + + public int getKeyNum() { + Preconditions.checkState(isKey); + return logicalNum; + } + + public int getInputColumnNum() { + return inputColumnNum; + } + + public int getLogicalNonKeyColumnNum() { + Preconditions.checkState(!isKey); + return logicalNum; + } + + public ColumnVector.Type getInputColumnVectorType() { + return inputColumnVectorType; + } + + public void addFunction(AggregationFunction aggregationFunction) { + aggregationFunctionsPresent[aggregationFunction.value] = true; + } + + public boolean[] getAggregationFunctionsPresent() { + return aggregationFunctionsPresent; + } + + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append("input column "); + sb.append(inputColumnNum); + sb.append(" isKey "); + sb.append(isKey); + sb.append(" aggregationFunctionsPresent"); + sb.append(Arrays.toString(aggregationFunctionsPresent)); + return sb.toString(); + } + } + + /** + * Information on each aggregation function being performed. + * + * It is the aggregation function output plus a reference to a column, which may be shared by + * other aggregation function outputs. + * + * If the column reference is null, it is COUNT(*). + */ + public static class AggregationFunctionInfo { + + private AggregationFunction aggregationFunction; + + private int funcNum; + + private TypeInfo outputTypeInfo; + + private ColumnAggregationInfo columnAggregationInfo; + + public AggregationFunctionInfo() { + this.aggregationFunction = null; + this.funcNum = -1; + this.outputTypeInfo = null; + this.columnAggregationInfo = null; + } + + public void set(AggregationFunction aggregationFunction, int funcNum, + TypeInfo outputTypeInfo) { + this.aggregationFunction = aggregationFunction; + this.funcNum = funcNum; + this.outputTypeInfo = outputTypeInfo; + } + + public void setColumnAggregationInfo(ColumnAggregationInfo columnAggregationInfo) { + this.columnAggregationInfo = columnAggregationInfo; + } + + public AggregationFunction getAggregationFunction() { + return aggregationFunction; + } + + public int getFuncNum() { + return funcNum; + } + + public TypeInfo getOutputTypeInfo() { + return outputTypeInfo; + } + + public ColumnAggregationInfo getColumnAggregationInfo() { + return columnAggregationInfo; + } + + @Override + public String toString() { + StringBuilder sb = new StringBuilder(); + sb.append("aggregation function "); + sb.append(aggregationFunction.name()); + sb.append(" funcNum "); + sb.append(funcNum); + sb.append(" output type "); + sb.append(outputTypeInfo); + if (columnAggregationInfo != null) { + sb.append(" column aggregation info "); + sb.append(columnAggregationInfo); + } + return sb.toString(); + } + } + + private ProcessingMode processingMode; + + private boolean keysAreAllFixedLength; + + private int[] groupByKeyColumnMap; + private TypeInfo[] groupByKeyTypeInfos; + private Type[] groupByKeyColumnVectorTypes; + private VectorExpression[] groupByKeyExpressions; + + private HashTableKeyType hashTableKeyType; + + private int[] aggregationFunctionInputColumns; + private TypeInfo[] aggregationFunctionInputTypeInfos; + private Type[] aggregationFunctionInputColumnVectorTypes; + private VectorExpression[] aggregationFunctionInputExpressions; + + private TypeInfo[] aggregationFunctionOutputTypeInfos; + + private boolean hasNonCountStar; + private boolean hasCountStar; + + private AggregationFunctionInfo[] aggregationFunctionInfos; + private HashMap columnAggregationMap; + + public VectorGroupByInfo() { + keysAreAllFixedLength = false; + + groupByKeyColumnMap = null; + groupByKeyTypeInfos = null; + groupByKeyColumnVectorTypes = null; + groupByKeyExpressions = null; + + hashTableKeyType = HashTableKeyType.NONE; + + aggregationFunctionInputColumns = null; + aggregationFunctionInputTypeInfos = null; + aggregationFunctionInputColumnVectorTypes = null; + aggregationFunctionInputExpressions = null; + + aggregationFunctionOutputTypeInfos = null; + + hasNonCountStar = false; + hasCountStar = false; + aggregationFunctionInfos = null; + columnAggregationMap = null; + } + + public ProcessingMode getProcessingMode() { + return processingMode; + } + + public void setProcessingMode(ProcessingMode processingMode) { + this.processingMode = processingMode; + } + + public boolean getKeysAreAllFixedLength() { + return keysAreAllFixedLength; + } + + public void setKeysAreAllFixedLength(boolean keysAreAllFixedLength) { + this.keysAreAllFixedLength = keysAreAllFixedLength; + } + + public int[] getGroupByKeyColumnMap() { + return groupByKeyColumnMap; + } + + public void setGroupByKeyColumnMap(int[] groupByKeyColumnMap) { + this.groupByKeyColumnMap = groupByKeyColumnMap; + } + + public TypeInfo[] getGroupByKeyTypeInfos() { + return groupByKeyTypeInfos; + } + + public void setGroupByKeyTypeInfos(TypeInfo[] groupByKeyTypeInfos) { + this.groupByKeyTypeInfos = groupByKeyTypeInfos; + } + + public Type[] getGroupByKeyColumnVectorTypes() { + return groupByKeyColumnVectorTypes; + } + + public void setGroupByKeyColumnVectorTypes(Type[] groupByKeyColumnVectorTypes) { + this.groupByKeyColumnVectorTypes = groupByKeyColumnVectorTypes; + } + + public VectorExpression[] getGroupByKeyExpressions() { + return groupByKeyExpressions; + } + + public void setGroupByKeyExpressions(VectorExpression[] groupByKeyExpressions) { + this.groupByKeyExpressions = groupByKeyExpressions; + } + + public HashTableKeyType getHashTableKeyType() { + return hashTableKeyType; + } + + public void setHashTableKeyType(HashTableKeyType hashTableKeyType) { + this.hashTableKeyType = hashTableKeyType; + } + + public int[] getAggregationFunctionInputColumns() { + return aggregationFunctionInputColumns; + } + + public void setAggregationFunctionInputColumns(int[] aggregationFunctionInputColumns) { + this.aggregationFunctionInputColumns = aggregationFunctionInputColumns; + } + + public TypeInfo[] getAggregationFunctionInputTypeInfos() { + return aggregationFunctionInputTypeInfos; + } + + public void setAggregationFunctionInputTypeInfos(TypeInfo[] aggregationFunctionInputTypeInfos) { + this.aggregationFunctionInputTypeInfos = aggregationFunctionInputTypeInfos; + } + + public Type[] getAggregationFunctionInputColumnVectorTypes() { + return aggregationFunctionInputColumnVectorTypes; + } + + public void setAggregationFunctionInputColumnVectorTypes(Type[] aggregationFunctionInputColumnVectorTypes) { + this.aggregationFunctionInputColumnVectorTypes = aggregationFunctionInputColumnVectorTypes; + } + + public VectorExpression[] getAggregationFunctionInputExpressions() { + return aggregationFunctionInputExpressions; + } + + public void setAggregationFunctionInputExpressions(VectorExpression[] aggregationFunctionInputExpressions) { + this.aggregationFunctionInputExpressions = aggregationFunctionInputExpressions; + } + + public TypeInfo[] getAggregationFunctionOutputTypeInfos() { + return aggregationFunctionOutputTypeInfos; + } + + public void setAggregationFunctionOutputTypeInfos(TypeInfo[] aggregationFunctionOutputTypeInfos) { + this.aggregationFunctionOutputTypeInfos = aggregationFunctionOutputTypeInfos; + } + + public AggregationFunctionInfo[] getAggregationFunctionInfos() { + return aggregationFunctionInfos; + } + + public void setAggregationFunctionInfos(AggregationFunctionInfo[] aggregationFunctionInfos) { + this.aggregationFunctionInfos = aggregationFunctionInfos; + } + + public HashMap getColumnAggregationMap() { + return columnAggregationMap; + } + + public void setColumnAggregationMap(HashMap columnAggregationMap) { + this.columnAggregationMap = columnAggregationMap; + } + + public boolean getHasNonCountStar() { + return hasNonCountStar; + } + + public void setHasNonCountStar(boolean hasNonCountStar) { + this.hasNonCountStar = hasNonCountStar; + } + + public boolean getHasCountStar() { + return hasCountStar; + } + + public void setHasCountStar(boolean hasCountStar) { + this.hasCountStar = hasCountStar; + } +} diff --git ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinDesc.java ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinDesc.java index e1bf1f4..cf5c79c 100644 --- ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinDesc.java +++ ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinDesc.java @@ -32,7 +32,6 @@ public static enum HashTableImplementationType { NONE, - OPTIMIZED, FAST } @@ -59,11 +58,14 @@ private HashTableKeyType hashTableKeyType; private boolean minMaxEnabled; + private VectorMapJoinInfo vectorMapJoinInfo; + public VectorMapJoinDesc() { hashTableImplementationType = HashTableImplementationType.NONE; hashTableKind = HashTableKind.NONE; hashTableKeyType = HashTableKeyType.NONE; minMaxEnabled = false; + vectorMapJoinInfo = null; } public VectorMapJoinDesc(VectorMapJoinDesc clone) { @@ -71,6 +73,7 @@ public VectorMapJoinDesc(VectorMapJoinDesc clone) { this.hashTableKind = clone.hashTableKind; this.hashTableKeyType = clone.hashTableKeyType; this.minMaxEnabled = clone.minMaxEnabled; + this.vectorMapJoinInfo = clone.vectorMapJoinInfo; } public HashTableImplementationType hashTableImplementationType() { @@ -104,4 +107,12 @@ public boolean minMaxEnabled() { public void setMinMaxEnabled(boolean minMaxEnabled) { this.minMaxEnabled = minMaxEnabled; } + + public void setVectorMapJoinInfo(VectorMapJoinInfo vectorMapJoinInfo) { + this.vectorMapJoinInfo = vectorMapJoinInfo; + } + + public VectorMapJoinInfo getVectorMapJoinInfo() { + return vectorMapJoinInfo; + } } diff --git ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinInfo.java ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinInfo.java new file mode 100644 index 0000000..eddefb1 --- /dev/null +++ ql/src/java/org/apache/hadoop/hive/ql/plan/VectorMapJoinInfo.java @@ -0,0 +1,124 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.plan; + +import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; + +/** + * VectorMapJoinInfo. + * + * A convenience data structure that has information needed to vectorize map join. + * + * It is created by the Vectorizer when it is determining whether it can specialize so the + * information doesn't have to be recreated again and again by the VectorMapJoinOperator's + * constructors and later during execution. + */ +public class VectorMapJoinInfo { + + private static long serialVersionUID = 1L; + + private int[] bigTableKeyColumnMap; + private String[] bigTableKeyColumnNames; + private TypeInfo[] bigTableKeyTypeInfos; + private VectorExpression[] bigTableKeyExpressions; + + private int[] bigTableValueColumnMap; + private String[] bigTableValueColumnNames; + private TypeInfo[] bigTableValueTypeInfos; + private VectorExpression[] bigTableValueExpressions; + + public VectorMapJoinInfo() { + bigTableKeyColumnMap = null; + bigTableKeyColumnNames = null; + bigTableKeyTypeInfos = null; + bigTableKeyExpressions = null; + + bigTableValueColumnMap = null; + bigTableValueColumnNames = null; + bigTableValueTypeInfos = null; + bigTableValueExpressions = null; + } + + public int[] getBigTableKeyColumnMap() { + return bigTableKeyColumnMap; + } + + public void setBigTableKeyColumnMap(int[] bigTableKeyColumnMap) { + this.bigTableKeyColumnMap = bigTableKeyColumnMap; + } + + public String[] getBigTableKeyColumnNames() { + return bigTableKeyColumnNames; + } + + public void setBigTableKeyColumnNames(String[] bigTableKeyColumnNames) { + this.bigTableKeyColumnNames = bigTableKeyColumnNames; + } + + public TypeInfo[] getBigTableKeyTypeInfos() { + return bigTableKeyTypeInfos; + } + + public void setBigTableKeyTypeInfos(TypeInfo[] bigTableKeyTypeInfos) { + this.bigTableKeyTypeInfos = bigTableKeyTypeInfos; + } + + public VectorExpression[] getBigTableKeyExpressions() { + return bigTableKeyExpressions; + } + + public void setBigTableKeyExpressions(VectorExpression[] bigTableKeyExpressions) { + this.bigTableKeyExpressions = bigTableKeyExpressions; + } + + + public int[] getBigTableValueColumnMap() { + return bigTableValueColumnMap; + } + + public void setBigTableValueColumnMap(int[] bigTableValueColumnMap) { + this.bigTableValueColumnMap = bigTableValueColumnMap; + } + + public String[] getBigTableValueColumnNames() { + return bigTableValueColumnNames; + } + + public void setBigTableValueColumnNames(String[] bigTableValueColumnNames) { + this.bigTableValueColumnNames = bigTableValueColumnNames; + } + + public TypeInfo[] getBigTableValueTypeInfos() { + return bigTableValueTypeInfos; + } + + public void setBigTableValueTypeInfos(TypeInfo[] bigTableValueTypeInfos) { + this.bigTableValueTypeInfos = bigTableValueTypeInfos; + } + + public VectorExpression[] getBigTableValueExpressions() { + return bigTableValueExpressions; + } + + public void setBigTableValueExpressions(VectorExpression[] bigTableValueExpressions) { + this.bigTableValueExpressions = bigTableValueExpressions; + } + +} \ No newline at end of file diff --git ql/src/java/org/apache/hadoop/hive/ql/plan/VectorReduceSinkInfo.java ql/src/java/org/apache/hadoop/hive/ql/plan/VectorReduceSinkInfo.java index 8c35415..807c26d 100644 --- ql/src/java/org/apache/hadoop/hive/ql/plan/VectorReduceSinkInfo.java +++ ql/src/java/org/apache/hadoop/hive/ql/plan/VectorReduceSinkInfo.java @@ -23,12 +23,12 @@ import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo; /** - * VectorGroupByAggregrationInfo. + * VectorReduceSinkInfo. * * A convenience data structure that has information needed to vectorize reduce sink. * * It is created by the Vectorizer when it is determining whether it can specialize so the - * information doesn't have to be recreated again and agains by the VectorReduceSinkOperator's + * information doesn't have to be recreated again and again by the VectorReduceSinkOperator's * constructors and later during execution. */ public class VectorReduceSinkInfo { @@ -36,22 +36,26 @@ private static long serialVersionUID = 1L; private int[] reduceSinkKeyColumnMap; + private String[] reduceSinkKeyColumnNames; private TypeInfo[] reduceSinkKeyTypeInfos; private Type[] reduceSinkKeyColumnVectorTypes; private VectorExpression[] reduceSinkKeyExpressions; private int[] reduceSinkValueColumnMap; + private String[] reduceSinkValueColumnNames; private TypeInfo[] reduceSinkValueTypeInfos; private Type[] reduceSinkValueColumnVectorTypes; private VectorExpression[] reduceSinkValueExpressions; public VectorReduceSinkInfo() { reduceSinkKeyColumnMap = null; + reduceSinkKeyColumnNames = null; reduceSinkKeyTypeInfos = null; reduceSinkKeyColumnVectorTypes = null; reduceSinkKeyExpressions = null; reduceSinkValueColumnMap = null; + reduceSinkValueColumnNames = null; reduceSinkValueTypeInfos = null; reduceSinkValueColumnVectorTypes = null; reduceSinkValueExpressions = null; @@ -65,6 +69,14 @@ public void setReduceSinkKeyColumnMap(int[] reduceSinkKeyColumnMap) { this.reduceSinkKeyColumnMap = reduceSinkKeyColumnMap; } + public String[] getReduceSinkKeyColumnNames() { + return reduceSinkKeyColumnNames; + } + + public void setReduceSinkKeyColumnNames(String[] reduceSinkKeyColumnNames) { + this.reduceSinkKeyColumnNames = reduceSinkKeyColumnNames; + } + public TypeInfo[] getReduceSinkKeyTypeInfos() { return reduceSinkKeyTypeInfos; } @@ -97,6 +109,14 @@ public void setReduceSinkValueColumnMap(int[] reduceSinkValueColumnMap) { this.reduceSinkValueColumnMap = reduceSinkValueColumnMap; } + public String[] getReduceSinkValueColumnNames() { + return reduceSinkValueColumnNames; + } + + public void setReduceSinkValueColumnNames(String[] reduceSinkValueColumnNames) { + this.reduceSinkValueColumnNames = reduceSinkValueColumnNames; + } + public TypeInfo[] getReduceSinkValueTypeInfos() { return reduceSinkValueTypeInfos; } diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestBytesBytesMultiHashMap.java ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestBytesBytesMultiHashMap.java index aed9214..63cef14 100644 --- ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestBytesBytesMultiHashMap.java +++ ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestBytesBytesMultiHashMap.java @@ -25,11 +25,16 @@ import java.util.List; import java.util.Random; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePutWriter; import org.apache.hadoop.hive.serde2.ByteStream.RandomAccessOutput; import org.apache.hadoop.hive.serde2.lazybinary.LazyBinaryUtils; import org.apache.hadoop.hive.serde2.SerDeException; import org.apache.hadoop.hive.serde2.WriteBuffers; +import org.apache.hadoop.io.Writable; +import org.apache.hive.common.util.HashCodeUtil; import org.junit.Test; + import static org.junit.Assert.*; public class TestBytesBytesMultiHashMap { @@ -52,10 +57,10 @@ public void testCapacityValidation() { public void testPutGetOne() throws Exception { BytesBytesMultiHashMap map = new BytesBytesMultiHashMap(CAPACITY, LOAD_FACTOR, WB_SIZE); RandomKvSource kv = new RandomKvSource(0, 0); - map.put(kv, -1); + map.put(kv); verifyHashMapResult(map, kv.getLastKey(), kv.getLastValue()); kv = new RandomKvSource(10, 100); - map.put(kv, -1); + map.put(kv); verifyHashMapResult(map, kv.getLastKey(), kv.getLastValue()); } @@ -63,12 +68,12 @@ public void testPutGetOne() throws Exception { public void testPutGetMultiple() throws Exception { BytesBytesMultiHashMap map = new BytesBytesMultiHashMap(CAPACITY, LOAD_FACTOR, WB_SIZE); RandomKvSource kv = new RandomKvSource(0, 100); - map.put(kv, -1); + map.put(kv); verifyHashMapResult(map, kv.getLastKey(), kv.getLastValue()); FixedKeyKvSource kv2 = new FixedKeyKvSource(kv.getLastKey(), 0, 100); kv2.values.add(kv.getLastValue()); for (int i = 0; i < 3; ++i) { - map.put(kv2, -1); + map.put(kv2); verifyHashMapResult(map, kv2.key, kv2.values.toArray(new byte[kv2.values.size()][])); } } @@ -76,17 +81,21 @@ public void testPutGetMultiple() throws Exception { @Test public void testGetNonExistent() throws Exception { BytesBytesMultiHashMap map = new BytesBytesMultiHashMap(CAPACITY, LOAD_FACTOR, WB_SIZE); + BytesBytesMultiHashMapFactory factory = new BytesBytesMultiHashMapFactory(); RandomKvSource kv = new RandomKvSource(1, 100); - map.put(kv, -1); + map.put(kv); byte[] key = kv.getLastKey(); key[0] = (byte)(key[0] + 1); FixedKeyKvSource kv2 = new FixedKeyKvSource(kv.getLastKey(), 0, 100); - map.put(kv2, -1); + map.put(kv2); key[0] = (byte)(key[0] + 1); - BytesBytesMultiHashMap.Result hashMapResult = new BytesBytesMultiHashMap.Result(); - map.getValueResult(key, 0, key.length, hashMapResult); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); assertTrue(!hashMapResult.hasRows()); - map.getValueResult(key, 0, 0, hashMapResult); + + hashCode = HashCodeUtil.murmurHash(key, 0, 0); + map.hashMapLookup(key, 0, 0, hashCode, hashMapResult); assertTrue(!hashMapResult.hasRows()); } @@ -94,17 +103,20 @@ public void testGetNonExistent() throws Exception { public void testPutWithFullMap() throws Exception { // Make sure the map does not expand; should be able to find space. BytesBytesMultiHashMap map = new BytesBytesMultiHashMap(CAPACITY, 1f, WB_SIZE); + BytesBytesMultiHashMapFactory factory = new BytesBytesMultiHashMapFactory(); UniqueKeysKvSource kv = new UniqueKeysKvSource(); for (int i = 0; i < CAPACITY; ++i) { - map.put(kv, -1); + map.put(kv); } for (int i = 0; i < kv.keys.size(); ++i) { verifyHashMapResult(map, kv.keys.get(i), kv.values.get(i)); } assertEquals(CAPACITY, map.getCapacity()); // Get of non-existent key should terminate.. - BytesBytesMultiHashMap.Result hashMapResult = new BytesBytesMultiHashMap.Result(); - map.getValueResult(new byte[0], 0, 0, hashMapResult); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + byte[] key = new byte[0]; + int hashCode = HashCodeUtil.murmurHash(key, 0, 0); + map.hashMapLookup(key, 0, 0, hashCode, hashMapResult); } @Test @@ -113,7 +125,7 @@ public void testExpand() throws Exception { BytesBytesMultiHashMap map = new BytesBytesMultiHashMap(1, 0.0000001f, WB_SIZE); UniqueKeysKvSource kv = new UniqueKeysKvSource(); for (int i = 0; i < 18; ++i) { - map.put(kv, -1); + map.put(kv); for (int j = 0; j <= i; ++j) { verifyHashMapResult(map, kv.keys.get(j), kv.values.get(j)); } @@ -121,9 +133,14 @@ public void testExpand() throws Exception { assertEquals(1 << 18, map.getCapacity()); } - private void verifyHashMapResult(BytesBytesMultiHashMap map, byte[] key, byte[]... values) { - BytesBytesMultiHashMap.Result hashMapResult = new BytesBytesMultiHashMap.Result(); - byte state = map.getValueResult(key, 0, key.length, hashMapResult); + private void verifyHashMapResult(BytesBytesMultiHashMap map, byte[] key, byte[]... values) + throws IOException { + BytesBytesMultiHashMapFactory factory = new BytesBytesMultiHashMapFactory(); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + byte state = hashMapResult.aliasFilter(); HashSet hs = new HashSet(); int count = 0; if (hashMapResult.hasRows()) { @@ -197,7 +214,7 @@ public void writeValue(RandomAccessOutput dest) throws SerDeException { } } - private static class RandomKvSource implements BytesBytesMultiHashMap.KvSource { + private static class RandomKvSource implements KeyValuePutWriter { private int minLength, maxLength; private final Random rdm = new Random(43); public List keys = new ArrayList(), values = new ArrayList(); @@ -241,5 +258,26 @@ public void writeValue(RandomAccessOutput dest) throws SerDeException { public byte updateStateByte(Byte previousValue) { return (byte)(previousValue == null ? 1 : previousValue + 1); } + + @Override + public void setKeyValue(Writable key, Writable value) + throws SerDeException, IOException { + throw new RuntimeException("Not used"); + } + + @Override + public boolean hasHashCode() { + return false; + } + + @Override + public int getKeyHashCode() throws SerDeException { + return 0; + } + + @Override + public long getLongKey() { + throw new RuntimeException("Not used"); + } } } diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestHashPartition.java ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestHashPartition.java index a6e52bd..1c2439d 100644 --- ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestHashPartition.java +++ ql/src/test/org/apache/hadoop/hive/ql/exec/persistence/TestHashPartition.java @@ -24,6 +24,7 @@ @Test public void testHashPartition() throws Exception { - HashPartition hashPartition = new HashPartition(1024, (float) 0.75, 524288, 1, true); + HashPartition hashPartition = new HashPartition(1024, (float) 0.75, 524288, 1, true, + new BytesBytesMultiHashMapFactory()); } } diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestUnsafe.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestUnsafe.java new file mode 100644 index 0000000..8276c55 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestUnsafe.java @@ -0,0 +1,66 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import static org.junit.Assert.assertTrue; +import junit.framework.TestCase; + + +/** + * Unit test for the vectorized conversion to and from row object[]. + */ +public class TestUnsafe extends TestCase { + + + + public void testUnsafe() throws Throwable { + + final int LONG_BYTES = Long.SIZE / Byte.SIZE; + + try { + boolean weAreUnsafe = UnsafeAccess.weAreUnsafe(); + if (weAreUnsafe) { + String string1 = "word"; + byte[] bytes1 = string1.getBytes(); + String string2 = "some more"; + byte[] bytes2 = string2.getBytes(); + long long1 = 12345; + long long2 = -98; + double double1 = 55.009; + double double2 = -10002; + long value1; + byte byte1_0; + + byte[] byteBuffer = new byte[1000]; + UnsafeAccess.putLong(byteBuffer, UnsafeAccess.BYTE_ARRAY_OFFSET + 0, long1); + value1 = UnsafeAccess.getLong(byteBuffer, UnsafeAccess.BYTE_ARRAY_OFFSET + 0); + assertTrue(value1 == long1); + + UnsafeAccess.copyMemory(bytes1, UnsafeAccess.BYTE_ARRAY_OFFSET, + byteBuffer, UnsafeAccess.BYTE_ARRAY_OFFSET + LONG_BYTES, bytes1.length); + byte1_0 = UnsafeAccess.getByte(byteBuffer, UnsafeAccess.BYTE_ARRAY_OFFSET + LONG_BYTES); + assertTrue(byte1_0 == 'w'); + } + + } catch (Throwable e) { + e.printStackTrace(); + throw e; + } + } +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestVectorGroupByVariableLenKey.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestVectorGroupByVariableLenKey.java new file mode 100644 index 0000000..ffaa727 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/TestVectorGroupByVariableLenKey.java @@ -0,0 +1,81 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import static org.junit.Assert.*; + +import org.apache.hadoop.hive.ql.exec.vector.keyseries.groupby.VectorGroupByLongsKey; +import org.junit.Test; + +/** + * Test vector group by variable length key class. + */ +public class TestVectorGroupByVariableLenKey { + + @Test + public void test() { + + byte[] bytes1 = "word".getBytes(); + byte[] bytes2 = "some stuffy".getBytes(); + byte[] bytes3 = "multi-long str".getBytes(); + byte[] bytes4 = "Test vector group by variable length key class".getBytes(); + + + long[] longs1 = new long[100]; + int testLongsLength1 = + VectorGroupByLongsKey.bytesToLongsKey(bytes1, 0, bytes1.length, longs1, 0); + assertEquals(1, testLongsLength1); + byte[] scratchBytes1 = new byte[100]; + VectorGroupByLongsKey.longsKeyToBytes(longs1, 0, scratchBytes1, 0); + assertTrue(equalBytes(bytes1, 0, scratchBytes1, 0, bytes1.length)); + + long[] longs2 = new long[100]; + int testLongsLength2 = + VectorGroupByLongsKey.bytesToLongsKey(bytes2, 5, 6, longs2, 0); + assertEquals(1, testLongsLength2); + byte[] scratchBytes2 = new byte[100]; + VectorGroupByLongsKey.longsKeyToBytes(longs2, 0, scratchBytes2, 0); + assertTrue(equalBytes(bytes2, 5, scratchBytes2, 0, 6)); + + long[] longs3 = new long[100]; + int testLongsLength3 = + VectorGroupByLongsKey.bytesToLongsKey(bytes3, 0, bytes3.length, longs3, 0); + assertEquals(2, testLongsLength3); + byte[] scratchBytes3 = new byte[100]; + VectorGroupByLongsKey.longsKeyToBytes(longs3, 0, scratchBytes3, 0); + assertTrue(equalBytes(bytes3, 0, scratchBytes3, 0, bytes3.length)); + + long[] longs4 = new long[100]; + int testLongsLength4 = + VectorGroupByLongsKey.bytesToLongsKey(bytes4, 0, bytes4.length, longs4, 0); + assertEquals(6, testLongsLength4); + byte[] scratchBytes4 = new byte[100]; + VectorGroupByLongsKey.longsKeyToBytes(longs4, 0, scratchBytes4, 0); + assertTrue(equalBytes(bytes4, 0, scratchBytes4, 0, bytes4.length)); + } + + private boolean equalBytes(byte[] bytes1, int start1, byte[] bytes2, int start2, int length) { + for (int i = 0; i < length; i++) { + if (bytes1[i + start1] != bytes2[i + start2]) { + return false; + } + } + return true; + } +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/UnsafeAccess.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/UnsafeAccess.java new file mode 100644 index 0000000..1ce7b34 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/UnsafeAccess.java @@ -0,0 +1,161 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby; + +import java.lang.reflect.Field; +import sun.misc.Unsafe; + +// This class is slightly modified version of org.apache.spark.unsafe.Platform + +public final class UnsafeAccess { + + private static final Unsafe _UNSAFE; + + public static final long BYTE_ARRAY_OFFSET; + + public static final long INT_ARRAY_OFFSET; + + public static final long LONG_ARRAY_OFFSET; + + public static final long DOUBLE_ARRAY_OFFSET; + + public static int getInt(Object object, long offset) { + return _UNSAFE.getInt(object, offset); + } + + public static void putInt(Object object, long offset, int value) { + _UNSAFE.putInt(object, offset, value); + } + + public static boolean getBoolean(Object object, long offset) { + return _UNSAFE.getBoolean(object, offset); + } + + public static void putBoolean(Object object, long offset, boolean value) { + _UNSAFE.putBoolean(object, offset, value); + } + + public static byte getByte(Object object, long offset) { + return _UNSAFE.getByte(object, offset); + } + + public static void putByte(Object object, long offset, byte value) { + _UNSAFE.putByte(object, offset, value); + } + + public static short getShort(Object object, long offset) { + return _UNSAFE.getShort(object, offset); + } + + public static void putShort(Object object, long offset, short value) { + _UNSAFE.putShort(object, offset, value); + } + + public static long getLong(Object object, long offset) { + return _UNSAFE.getLong(object, offset); + } + + public static void putLong(Object object, long offset, long value) { + _UNSAFE.putLong(object, offset, value); + } + + public static float getFloat(Object object, long offset) { + return _UNSAFE.getFloat(object, offset); + } + + public static void putFloat(Object object, long offset, float value) { + _UNSAFE.putFloat(object, offset, value); + } + + public static double getDouble(Object object, long offset) { + return _UNSAFE.getDouble(object, offset); + } + + public static void putDouble(Object object, long offset, double value) { + _UNSAFE.putDouble(object, offset, value); + } + + public static Object getObjectVolatile(Object object, long offset) { + return _UNSAFE.getObjectVolatile(object, offset); + } + + public static void putObjectVolatile(Object object, long offset, Object value) { + _UNSAFE.putObjectVolatile(object, offset, value); + } + + public static long allocateMemory(long size) { + return _UNSAFE.allocateMemory(size); + } + + public static void freeMemory(long address) { + _UNSAFE.freeMemory(address); + } + + public static void copyMemory( + Object src, long srcOffset, Object dst, long dstOffset, long length) { + while (length > 0) { + long size = Math.min(length, UNSAFE_COPY_THRESHOLD); + _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size); + length -= size; + srcOffset += size; + dstOffset += size; + } + } + + /** + * Raises an exception bypassing compiler checks for checked exceptions. + */ + public static void throwException(Throwable t) { + _UNSAFE.throwException(t); + } + + /** + * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to + * allow safepoint polling during a large copy. + */ + private static final long UNSAFE_COPY_THRESHOLD = 1024L * 1024L; + + static { + sun.misc.Unsafe unsafe; + try { + Field unsafeField = Unsafe.class.getDeclaredField("theUnsafe"); + unsafeField.setAccessible(true); + unsafe = (sun.misc.Unsafe) unsafeField.get(null); + } catch (Throwable cause) { + unsafe = null; + } + _UNSAFE = unsafe; + + if (_UNSAFE != null) { + BYTE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(byte[].class); + INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class); + LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class); + DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class); + } else { + BYTE_ARRAY_OFFSET = 0; + INT_ARRAY_OFFSET = 0; + LONG_ARRAY_OFFSET = 0; + DOUBLE_ARRAY_OFFSET = 0; + } + } + + public static boolean weAreUnsafe() { + return (_UNSAFE != null); + } +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/keyseries/TestVectorKeySeries.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/keyseries/TestVectorKeySeries.java new file mode 100644 index 0000000..01e5818 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/groupby/keyseries/TestVectorKeySeries.java @@ -0,0 +1,51 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.groupby.keyseries; + +import org.apache.hadoop.hive.ql.exec.vector.LongColumnVector; +import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch; +import org.apache.hadoop.hive.ql.exec.vector.keyseries.VectorKeySeriesLong; +import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory; + +import junit.framework.TestCase; + +/** + * Unit test for the vectorized key series. + */ +public class TestVectorKeySeries extends TestCase { + + public void testVectorKeySeries() throws Throwable { + + try { + VectorizedRowBatch batch = new VectorizedRowBatch(1); + + long[] test0 = {1906L, -7838598833900584960L, 1165L, -7456869587112255488L, 2013L, 7333512171174223872L, -7571293705217687552L, -8523434203900674048L, 8099215208813903872L, 9040958359122640896L, -7356685674003021824L, 2072L, 2073L, 871L, -7551394356730339328L, -8172827216441573376L, -8082793390939193344L, 8854495099223375872L, -8358130693961195520L, 9050032047355125760L, -7162299524557471744L, 809L, -8946656952763777024L, 1053L, 482L, -6968892545529896960L, 203L, 1614L, -8593419958317056000L, 8190967051000659968L, 808L, 1L, 412L, 8656571350884048896L, 8769199243315814400L, -8546758906409312256L, 8829545979081744384L, 7545689659010949120L, 618L, 8573305425181941760L, 94L, -7266719102957125632L, 2772L, 379L, 8302473563519950848L, -7802538500225777664L, -7273694358642851840L, 8987827141270880256L, 914L, 723L, -7104310188119834624L, 154L, -8494118409594650624L, 3781L, 1466L, 724L, -7270034223527993344L, 913L, 1704L, 1L, -7883252982752665600L, 412L, 4024L, 7226360892091416576L, -8244657976255889408L, 2862L, 1521L, 1L, 7386087924003676160L, 3907L, 7818464507324121088L, -8293833565967810560L, -7892780594910871552L, 1509L, 7592440105065308160L, -7600138468036386816L, 9064847977742032896L, -8379964450833367040L, 7217123582035116032L, -7879864376629567488L, 2878L, 2412L, 524L, 784L, -7046180371529351168L, 471L, 612L, 8368012468775608320L, -7547245548870025216L, 3841L, 8752150411997356032L, -8623965248051789824L, 7637152193832886272L, 9191943992860327936L, 2700L, 9180098147855769600L, 1775L, 797L, -7773957003968675840L, -8660149447361404928L, 8641221723991433216L, 392L, 1L, 8489735221193138176L, 7944741547145502720L, 6933731240564056064L, 9083704659251798016L, -9084940280061485056L, 8222714144797368320L, 8817665768680906752L, 1995L, 1561L, 2485L, 1826L, 845L, 8376440110255243264L, 9075404705968840704L, -8379109122834997248L, -6938706403992854528L, 961L, 1422L, 9149216169284091904L, 2752L, 2255L, -9080568167841226752L, 1046L, 7926898770090491904L, 7784489776013295616L, 6991316084916879360L, 1566L, 1671L, -8543982423727128576L, -8832750849949892608L, 6963217546192322560L, 236L, 7086206629592252416L, 9053187076403060736L, -8067243114610532352L, 1751L, 2502L, 294L, 7892281003266408448L, 8577096957495025664L, -8665764757143658496L, 2855L, 2811L, 8785153741735616512L, 1726L, 7186401810812059648L, -7603569103205916672L, 4018L, 3566L, 2725L, 1234L, 346L, 7961515985722605568L, 7274777328897802240L, -6933565857643814912L, -8330233444291084288L, 34L, 7080269176324218880L, 2941L, 9117063974299148288L, -6917607783359897600L, -8566940231897874432L, -8710298418608619520L, 1520L, 3728L, -8835408234247168000L, 7705445437881278464L, 6926925215281774592L, 835L, 1L, 3232L, -7840338174858199040L, 7748799008146366464L, 7410096605330227200L, 188L, 1L, -7709958788604936192L, -6920172215209426944L, -9109392978217484288L, 3608L, -8214462866994339840L, 2306L, -7759238919361888256L, -8922409715403112448L, 3664L, -9203942396257984512L, 8116738401948377088L, 1791L, -7419068456205385728L, 8795069490394882048L, 3043L, 3174L, 7625728883085025280L, -8585134536083660800L, 8558000156325707776L, -8572949572756774912L, 661L, 2393L, -7800879252150779904L, 7534549597202194432L, -7642381493746483200L, -7330413050756235264L, 7596563216912211968L, 3307L, 2971L, 2285L, 1880L, 4088L, 743L, -8317591428117274624L, 8854715632851345408L, 7768984605670604800L, 2900L, 7062605127422894080L, 7394967727502467072L, 1781L, 7238339720750948352L, 1638L, 1L, -8522878384019169280L, -8051587217208967168L, -7425160895830573056L, 7344029858387820544L, -8013397854633648128L, 8808467247666241536L, -8768744394742235136L, 9185458640237641728L, -7686220526274502656L, -8203075743525806080L, 3462L, 6964585306125008896L, 3418L, 3366L, -7867219225874571264L, 8367680396909404160L, 7524958388842078208L, 2897L, 8391785334471589888L, -8581979259158929408L, 587L, 130L, 1030L, 8362046808797306880L, 3691L, 7454632396542074880L, 7125231541858205696L, 2580L, 2512L, 7061498706968428544L, -7255686273677328384L, 9048002942653710336L, 8868529429494071296L, 8815398225009967104L, 7128222874437238784L, 8371939471056470016L, -8335810316927213568L, -7144791190333546496L, 1L, 1L, -7572962089372991488L, 8850055384477401088L, 2626L, 3599L, 213L, 2232L, -8297230235506343936L, 3430L, 391L, -7395343938785738752L, 9038087402564657152L, -9013952631912325120L, 3446L, -8703026916864802816L, -7833618000492109824L, 1541L, 8759184090543857664L, -7042183597114081280L, -7147490721376591872L, 3725L, 7961909238130270208L, -8930307926221807616L, 2719L, -6988970700649168896L, -7155539549555105792L, 3625L, 8113585123802529792L, 9207927479837319168L, -8387347109404286976L, 1L, 8896237972875370496L, 8372408423196270592L, 922L, 7255302164215013376L, -8585966098173870080L, 8424515140664360960L, -6997233584896229376L, 8087737899452432384L, 1493L, 8779711700787298304L, 2533L, 1L, 8017403886247927808L, 1282L, 2177L, -8632237187473088512L, 8109381965028548608L, 1157L, 7378993334503694336L, 1L, 2560L, 4037L, -8562524688907485184L, 2325L, 6962726713896484864L, 8120593157178228736L, 6924820982050758656L, -7366430883634929664L, -7209060152494817280L, -8689606130068611072L, 3190L, 3725L, -8581765103969312768L, 1L, 3542L, 8553195689344991232L, 1789L, 8698055291501543424L, 296L, -9095689235523264512L, 7998687089080467456L, 8160569434550403072L, 489L, -9175038118837149696L, 8571268359622172672L, -7916510129632296960L, 8323460620425330688L, 346L, 3980L, -7707242953271500800L, 1811L, 2803L, 7370078518278397952L, 7497276415392407552L, 2323L, 8467976965865799680L, 691L, 1914L, 6982145326341423104L, -9203804401302323200L, 7823874904139849728L, 7534145866886782976L, 9085434340468473856L, 8579974641030365184L, 8536948829863198720L, 341L, -9102482277760983040L, 658L, 1L, 2843L, 7584007864107778048L, 590L, 8899122608190930944L, 3588L, 3609L, 3824L, 7690986322714066944L, 7765456790394871808L, -8649711322250362880L, 1948L, -9101953184875757568L, 2463L, 1813L, 7054271419461812224L, 7548958830580563968L, -9206329156028112896L, 2637L, -7661250850555633664L, 664L, 2487L, 8221561626658881536L, 8169878743136043008L, 6927260280037097472L, 342L, -7501803640821456896L, 2745L, 677L, 8435912708683087872L, 7412924364686458880L, 3563L, 1L, 7153922334283776000L, 8849475396952514560L, 2977L, -7910019233726242816L, 2835L, 2335L, 1L, 2515L, -7617860842651017216L, -7637755520917741568L, 2647L, 707L, 8856674723376668672L, 7857878068300898304L, -8887058200926093312L, 108L, 2762L, 3622L, 868L, 138L, 1786L, 9116137265342169088L, 7955126053367119872L, 491L, -7778829032042790912L, -7753051494275432448L, 8962097525980225536L, 8163948965373386752L, 1145L, -8438554249514491904L, 522L, 1785L, 1545L, 999L, 1941L, 1L, 7454442625055145984L, 3510L, 2373L, -8127494999848919040L, 1643L, -7819437864839495680L, -7822452149325094912L, 7411793502161182720L, 2274L, 8783241818558193664L, 8316336224427483136L, -7669169138124275712L, 2984L, -7772064021830574080L, 3397L, 1L, 8523972434954510336L, -7127548949860818944L, 8286706213485297664L, 3147L, -7536330682873937920L, -7115054815375073280L, -7319315187617587200L, 1099L, -8989473881707921408L, 2816L, -6986178228432322560L, -7759425383684849664L, -7893577088764174336L, 8091421389575282688L, -7409653086454030336L, 7348598907182800896L, -7362189611124563968L, 1L, 2465L, 350L, 2619L, 3722L, 898L, 782L, 1780L, 2186L, -6921654334727036928L, 4020L, 8325227661920133120L, -7036607470351654912L, 7304839835188609024L, 8792059919353348096L, -8856821118526734336L, 8720504651219001344L, 1055L, 1368L, 8736061027343859712L, 7919597361814577152L, 7381659098423926784L, 8731960288562044928L, -7594824008626372608L, -9178166810751909888L, 3083L, -8948335470186373120L, 2569L, 823L, 259L, 8461498293348065280L, -8961059046745669632L, -8607195685207408640L, -8754966081778565120L, -8418913260807217152L, -8877053610728161280L, -6935548339131138048L, -8219876839318716416L, 1132L, 1337L, 1341L, 976L, -7557017910095650816L, 1L, -8683802826440105984L, 1845L, 1965L, -8104684579106914304L, 1835L, 7345991518378442752L, 3212L, -7081500255163727872L, 1074L, 8372588378498777088L, -7593363318079610880L, -7451660755269853184L, 1983L, 8514851182589771776L, 1864L, 8463868417649524736L, 3094L, -8858063395050110976L, 1981L, -8140349174954893312L, -7041362811802148864L, 8972161729142095872L, 7989119273552158720L, 2469L, 1481L, -8566856504746352640L, 8272001752345690112L, -7094827141662539776L, 8396433451610652672L, -7679894005808693248L, 8613562211893919744L, 3407L, 7686992843032010752L, 1048L, 3507L, 7784169796350730240L, 8551446856960942080L, 3467L, 1458L, 213L, 735L, 9190466190353661952L, -8280276629934981120L, -7895991410072928256L, -9145593811310010368L, 8059284960252731392L, 367L, 7614435638888210432L, 9174894805640142848L, -8941201923743703040L, 1075L, 7492436934952574976L, -8714995808835444736L, 7782245855193874432L, 8525894870444638208L, -7661192563533062144L, 1L, 8995562121346260992L, 7626715182847090688L, 8146492373537660928L, 7682327310082531328L, 2968L, 7309156463509061632L, 1955L, 1L, 7022349041913978880L, 7045967493826387968L, 3006L, 65L, 8168742078705262592L, 7212016545671348224L, 8079573715140485120L, 3965L, 8555933456197828608L, 2903L, 7648729477297987584L, 1L, 8223732800007864320L, -7412431471807283200L, 2560L, 2988L, 1243L, 1837L, 7014537632150224896L, 3747L, 2682L, 8073733016154431488L, 2938L, 1312L, 7006803044329021440L, 7701723309715685376L, 7528074274555305984L, -7532751268425261056L, 8000440057238052864L, -7964801953178091520L, 2846L, 8723248113030782976L, 7440265908266827776L, 927L, -7063777488249085952L, 9194388393453060096L, 7720187583697502208L, 8557218322962644992L, 950L, 2189L, 1371L, 7370803940448305152L, -8914039133569400832L, 3663L, 2341L, -8877431933441327104L, 8171188598958407680L, 8525336514806317056L, 1608L, -7094189393339678720L, 1752L, 3084L, 3673L, 9169248521377374208L, -7866079955473989632L, -9004892183139811328L, 1892L, 6928080429732536320L, -7623047151287754752L, 2492L, -7695491171376291840L, -7797151404935618560L, 8208354137450766336L, -7395553021620731904L, -8453491903284994048L, -7140008543769042944L, 2724L, 3443L, -7512297136103800832L, 9136234417125007360L, 8192304692696383488L, 8199513544090730496L, 311L, -8488247955875618816L, 1L, 2540L, 586L, -7444070205513138176L, 1141L, -8076479329071955968L, 3103L, -7629401308029976576L, -7507424948896415744L, 2821L, 2017L, 1134L, 347L, -7246123871306244096L, 2020L, 1693L, 2020L, 8570983266408103936L, 2919L, 2283L, 7534042483076857856L, 1L, 8991442360387584000L, -7240213957902663680L, 3365L, 1899L, 7199539820886958080L, 7165364563962191872L, 8407869317250220032L, 1489L, 2400L, -7037375807670501376L, 7235109456886816768L, 8569030475428511744L, 2067L, 8332670681629106176L, 168L, 1L, -83}; + LongColumnVector longColVector = new LongColumnVector(VectorizedRowBatch.DEFAULT_SIZE); + batch.cols[0] = longColVector; + System.arraycopy(test0, 0, longColVector.vector, 0, test0.length); + + VectorKeySeriesLong longKeySeries = new VectorKeySeriesLong(0, TypeInfoFactory.longTypeInfo); + longKeySeries.processBatch(batch); + System.out.println("longKeySeries duplicateCount " + longKeySeries.getCurrentDuplicateCount() + " isNull " + longKeySeries.getCurrentIsAllNull()); + } catch (Throwable e) { + e.printStackTrace(); + throw e; + } + } +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/CommonFastHashTable.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/CommonFastHashTable.java index c2375e0..64b0607 100644 --- ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/CommonFastHashTable.java +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/CommonFastHashTable.java @@ -23,7 +23,7 @@ import java.util.List; import java.util.Random; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; import org.apache.hadoop.hive.serde2.WriteBuffers; import static org.junit.Assert.*; @@ -39,6 +39,8 @@ protected static final int LARGE_CAPACITY = 8388608; protected static Random random; + + public static int generateLargeCount() { int count = 0; if (random.nextInt(100) != 0) { @@ -75,7 +77,7 @@ public static int generateLargeCount() { } return count; } - public static void verifyHashMapResult(VectorMapJoinHashMapResult hashMapResult, + public static void verifyHashMapResult(MapJoinHashMapResult hashMapResult, RandomByteArrayStream randomByteArrayStream ) { List resultBytes = new ArrayList(); @@ -105,7 +107,7 @@ public static void verifyHashMapResult(VectorMapJoinHashMapResult hashMapResult, } } - public static void verifyHashMapResult(VectorMapJoinHashMapResult hashMapResult, + public static void verifyHashMapResult(MapJoinHashMapResult hashMapResult, byte[] valueBytes ) { assertTrue(hashMapResult.hasRows()); diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestKeyValueWriter.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestKeyValueWriter.java new file mode 100644 index 0000000..a9c05b5 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestKeyValueWriter.java @@ -0,0 +1,87 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableManage.KeyValuePutWriter; +import org.apache.hadoop.hive.serde2.ByteStream.RandomAccessOutput; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.Writable; +import org.apache.hive.common.util.HashCodeUtil; + +public class TestKeyValueWriter implements KeyValuePutWriter { + + private byte[] key; + private byte[] value; + + public TestKeyValueWriter() { + } + + void setKeyValue(byte[] key, byte[] value) { + this.key = key; + this.value = value; + } + + @Override + public void writeKey(RandomAccessOutput dest) throws SerDeException { + try { + dest.write(key); + } catch (Exception e) { + throw new SerDeException(e); + } + } + + @Override + public void writeValue(RandomAccessOutput dest) throws SerDeException { + try { + dest.write(value); + } catch (Exception e) { + throw new SerDeException(e); + } + } + + @Override + public byte updateStateByte(Byte previousValue) { + return 0; + } + + @Override + public void setKeyValue(Writable key, Writable value) throws SerDeException, + IOException { + throw new RuntimeException("Not used"); + } + + @Override + public boolean hasHashCode() { + return true; + } + + @Override + public int getKeyHashCode() throws SerDeException { + return HashCodeUtil.murmurHash(key, 0, key.length); + } + + @Override + public long getLongKey() { + throw new RuntimeException("Not used"); + } + +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestLongKeyValueWriter.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestLongKeyValueWriter.java new file mode 100644 index 0000000..273fa51 --- /dev/null +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestLongKeyValueWriter.java @@ -0,0 +1,82 @@ +/** + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; + +import java.io.IOException; + +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.serde2.ByteStream.RandomAccessOutput; +import org.apache.hadoop.hive.serde2.lazybinary.LazyBinaryUtils; +import org.apache.hadoop.hive.serde2.SerDeException; +import org.apache.hadoop.io.Writable; +import org.apache.hive.common.util.HashCodeUtil; + +public class TestLongKeyValueWriter implements MapJoinHashTable.KeyValuePutWriter { + + private long key; + private byte[] value; + + public TestLongKeyValueWriter() { + } + + void setLongKeyValue(long key, byte[] value) { + this.key = key; + this.value = value; + } + + @Override + public void writeKey(RandomAccessOutput dest) throws SerDeException { + LazyBinaryUtils.writeVLong(dest, key); + } + + @Override + public void writeValue(RandomAccessOutput dest) throws SerDeException { + try { + dest.write(value); + } catch (Exception e) { + throw new SerDeException(e); + } + } + + @Override + public byte updateStateByte(Byte previousValue) { + return 0; + } + + @Override + public void setKeyValue(Writable key, Writable value) throws SerDeException, + IOException { + throw new RuntimeException("Not used"); + } + + @Override + public boolean hasHashCode() { + return true; + } + + @Override + public int getKeyHashCode() throws SerDeException { + return HashCodeUtil.calculateLongHashCode(key); + } + + @Override + public long getLongKey() { + throw new RuntimeException("Not used"); + } +} \ No newline at end of file diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastLongHashMap.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastLongHashMap.java index a45275b..73b659d 100644 --- ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastLongHashMap.java +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastLongHashMap.java @@ -18,12 +18,15 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import java.io.IOException; import java.util.Random; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast.VectorMapJoinFastLongHashMap; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hive.common.util.HashCodeUtil; import org.junit.Test; import static org.junit.Assert.*; @@ -34,42 +37,61 @@ public void testPutGetOne() throws Exception { random = new Random(47496); - VectorMapJoinFastLongHashMap map = - new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + + MapJoinHashTable map = + factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); long key = randomLongKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream.get(0)); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream.get(0), + hashMapResult); key = randomLongKeyStream.next(); value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream.get(1)); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream.get(1), + hashMapResult); } @Test public void testPutGetMultiple() throws Exception { random = new Random(2990); - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + + MapJoinHashTable map = + factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); long key = randomLongKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); + + verifyHashMapResult(map, key, value, hashMapResult); // Same key, multiple values. for (int i = 0; i < 3; ++i) { value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream, + hashMapResult); } } @@ -77,22 +99,31 @@ public void testPutGetMultiple() throws Exception { public void testGetNonExistent() throws Exception { random = new Random(16916); - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + + MapJoinHashTable map = + factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); long key = randomLongKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); key += 1; - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); key += 1; - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, hashMapResult); - assertTrue(joinResult == JoinUtil.JoinResult.NOMATCH); + int hashCode = HashCodeUtil.calculateLongHashCode(key); + map.hashMapLookup(key, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + assertTrue(lookupResult == MapJoinHashTableResult.MapJoinResult.NO_MATCH); assertTrue(!hashMapResult.hasRows()); } @@ -100,33 +131,48 @@ public void testGetNonExistent() throws Exception { public void testPutWithFullMap() throws Exception { random = new Random(26078); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + // Make sure the map does not expand; should be able to find space. - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, CAPACITY, 1f, WB_SIZE); + MapJoinHashTable map = + factory.createHashTable(CAPACITY, 1f, WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); for (int i = 0; i < CAPACITY; ++i) { long key = randomLongKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); } for (int i = 0; i < randomLongKeyStream.size(); ++i) { - verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStream.get(i)); + verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStream.get(i), + hashMapResult); } // assertEquals(CAPACITY, map.getCapacity()); // Get of non-existent key should terminate.. long anotherKey = randomLongKeyStream.next(); - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(anotherKey, hashMapResult); - assertTrue(joinResult == JoinUtil.JoinResult.NOMATCH); + int hashCode = HashCodeUtil.calculateLongHashCode(anotherKey); + map.hashMapLookup(anotherKey, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + assertTrue(lookupResult == MapJoinHashTableResult.MapJoinResult.NO_MATCH); } @Test public void testExpand() throws Exception { random = new Random(22470); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + // Start with capacity 1; make sure we expand on every put. - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, 1, 0.0000001f, WB_SIZE); + MapJoinHashTable map = + factory.createHashTable(1, 0.0000001f, WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); @@ -134,9 +180,11 @@ public void testExpand() throws Exception { for (int i = 0; i < 18; ++i) { long key = randomLongKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); for (int j = 0; j <= i; ++j) { - verifyHashMapResult(map, randomLongKeyStream.get(j), randomByteArrayValueStream.get(j)); + verifyHashMapResult(map, randomLongKeyStream.get(j), randomByteArrayValueStream.get(j), + hashMapResult); } } // assertEquals(1 << 18, map.getCapacity()); @@ -146,8 +194,14 @@ public void testExpand() throws Exception { public void testLarge() throws Exception { random = new Random(40719); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + // Use a large capacity that doesn't require expansion, yet. - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, LARGE_CAPACITY, LOAD_FACTOR, LARGE_WB_SIZE); + MapJoinHashTable map = + factory.createHashTable(LARGE_CAPACITY, LOAD_FACTOR, LARGE_WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); @@ -159,11 +213,13 @@ public void testLarge() throws Exception { long key = randomLongKeyStream.next(); for (int v = 0; v < count; v++) { byte[] value = randomByteArrayValueStreams[i].next(); - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); } } for (int i = 0; i < largeSize; i++) { - verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStreams[i]); + verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStreams[i], + hashMapResult); } } @@ -171,8 +227,15 @@ public void testLarge() throws Exception { public void testLargeAndExpand() throws Exception { random = new Random(46809); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.LONG); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestLongKeyValueWriter kvWriter = new TestLongKeyValueWriter(); + // Use a large capacity that doesn't require expansion, yet. - VectorMapJoinFastLongHashMap map = new VectorMapJoinFastLongHashMap(false, false, HashTableKeyType.LONG, MODERATE_CAPACITY, LOAD_FACTOR, MODERATE_WB_SIZE); + MapJoinHashTable map = + factory.createHashTable( + MODERATE_CAPACITY, LOAD_FACTOR, MODERATE_WB_SIZE, 0); RandomLongStream randomLongKeyStream = new RandomLongStream(random); @@ -184,32 +247,40 @@ public void testLargeAndExpand() throws Exception { long key = randomLongKeyStream.next(); for (int v = 0; v < count; v++) { byte[] value = randomByteArrayValueStreams[i].next(); - map.putRow(key, value); + kvWriter.setLongKeyValue(key, value); + map.put(kvWriter); } } for (int i = 0; i < largeSize; i++) { - verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStreams[i]); + verifyHashMapResult(map, randomLongKeyStream.get(i), randomByteArrayValueStreams[i], + hashMapResult); } } - private void verifyHashMapResult(VectorMapJoinFastLongHashMap map, long key, - RandomByteArrayStream randomByteArrayValueStream) { + private void verifyHashMapResult(MapJoinHashTable map, long key, + RandomByteArrayStream randomByteArrayValueStream, + MapJoinHashMapResult hashMapResult) + throws IOException { - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, hashMapResult); - if (joinResult != JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.calculateLongHashCode(key); + map.hashMapLookup(key, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult != MapJoinHashTableResult.MapJoinResult.MATCH) { assertTrue(false); } CommonFastHashTable.verifyHashMapResult(hashMapResult, randomByteArrayValueStream); } - private void verifyHashMapResult(VectorMapJoinFastLongHashMap map, long key, - byte[] valueBytes) { + private void verifyHashMapResult(MapJoinHashTable map, + long key, byte[] valueBytes, + MapJoinHashMapResult hashMapResult) + throws IOException { - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, hashMapResult); - if (joinResult != JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.calculateLongHashCode(key); + map.hashMapLookup(key, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult != MapJoinHashTableResult.MapJoinResult.MATCH) { assertTrue(false); } diff --git ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastMultiKeyHashMap.java ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastMultiKeyHashMap.java index 944bda6..4fe79dc 100644 --- ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastMultiKeyHashMap.java +++ ql/src/test/org/apache/hadoop/hive/ql/exec/vector/mapjoin/fast/TestVectorMapJoinFastMultiKeyHashMap.java @@ -18,12 +18,14 @@ package org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast; +import java.io.IOException; import java.util.Random; -import org.apache.hadoop.hive.ql.exec.JoinUtil; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult; -import org.apache.hadoop.hive.ql.exec.vector.mapjoin.fast.VectorMapJoinFastMultiKeyHashMap; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashMapResult; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTable; +import org.apache.hadoop.hive.ql.exec.persistence.mapjoinhashtable.MapJoinHashTableResult; import org.apache.hadoop.hive.ql.plan.VectorMapJoinDesc.HashTableKeyType; +import org.apache.hive.common.util.HashCodeUtil; import org.junit.Test; import static org.junit.Assert.*; @@ -34,42 +36,61 @@ public void testPutGetOne() throws Exception { random = new Random(47496); - VectorMapJoinFastMultiKeyHashMap map = - new VectorMapJoinFastMultiKeyHashMap(false, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); byte[] key = randomByteArrayKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream.get(0)); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream.get(0), + hashMapResult); key = randomByteArrayKeyStream.next(); value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream.get(1)); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream.get(1), + hashMapResult); } @Test public void testPutGetMultiple() throws Exception { random = new Random(2990); - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); byte[] key = randomByteArrayKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, value, + hashMapResult); // Same key, multiple values. for (int i = 0; i < 3; ++i) { value = randomByteArrayValueStream.next(); - map.putRow(key, value); - verifyHashMapResult(map, key, randomByteArrayValueStream); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); + verifyHashMapResult(map, key, randomByteArrayValueStream, + hashMapResult); } } @@ -77,22 +98,31 @@ public void testPutGetMultiple() throws Exception { public void testGetNonExistent() throws Exception { random = new Random(16916); - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, CAPACITY, LOAD_FACTOR, WB_SIZE); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(CAPACITY, LOAD_FACTOR, WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); byte[] key = randomByteArrayKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); key[0] = (byte) (key[0] + 1); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); key[0] = (byte) (key[0] + 1); - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, 0, key.length, hashMapResult); - assertTrue(joinResult == JoinUtil.JoinResult.NOMATCH); + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + assertTrue(lookupResult == MapJoinHashTableResult.MapJoinResult.NO_MATCH); assertTrue(!hashMapResult.hasRows()); } @@ -100,33 +130,48 @@ public void testGetNonExistent() throws Exception { public void testPutWithFullMap() throws Exception { random = new Random(26078); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + // Make sure the map does not expand; should be able to find space. - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, CAPACITY, 1f, WB_SIZE); + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(CAPACITY, 1f, WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); for (int i = 0; i < CAPACITY; ++i) { byte[] key = randomByteArrayKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); } for (int i = 0; i < randomByteArrayKeyStream.size(); ++i) { - verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStream.get(i)); + verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStream.get(i), + hashMapResult); } // assertEquals(CAPACITY, map.getCapacity()); // Get of non-existent key should terminate.. byte[] anotherKey = randomByteArrayKeyStream.next(); - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(anotherKey, 0, anotherKey.length, hashMapResult); - assertTrue(joinResult == JoinUtil.JoinResult.NOMATCH); + int hashCode = HashCodeUtil.murmurHash(anotherKey, 0, anotherKey.length); + map.hashMapLookup(anotherKey, 0, anotherKey.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + assertTrue(lookupResult == MapJoinHashTableResult.MapJoinResult.NO_MATCH); } @Test public void testExpand() throws Exception { random = new Random(22470); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + // Start with capacity 1; make sure we expand on every put. - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, 1, 0.0000001f, WB_SIZE); + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(1, 0.0000001f, WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random); RandomByteArrayStream randomByteArrayValueStream = new RandomByteArrayStream(random); @@ -134,9 +179,11 @@ public void testExpand() throws Exception { for (int i = 0; i < 18; ++i) { byte[] key = randomByteArrayKeyStream.next(); byte[] value = randomByteArrayValueStream.next(); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); for (int j = 0; j <= i; ++j) { - verifyHashMapResult(map, randomByteArrayKeyStream.get(j), randomByteArrayValueStream.get(j)); + verifyHashMapResult(map, randomByteArrayKeyStream.get(j), randomByteArrayValueStream.get(j), + hashMapResult); } } // assertEquals(1 << 18, map.getCapacity()); @@ -146,8 +193,14 @@ public void testExpand() throws Exception { public void testLarge() throws Exception { random = new Random(5231); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + // Use a large capacity that doesn't require expansion, yet. - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, LARGE_CAPACITY, LOAD_FACTOR, LARGE_WB_SIZE); + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(LARGE_CAPACITY, LOAD_FACTOR, LARGE_WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random, 10); @@ -157,19 +210,22 @@ public void testLarge() throws Exception { randomByteArrayValueStreams[i] = new RandomByteArrayStream(random); int count = generateLargeCount(); byte[] key = randomByteArrayKeyStream.next(); - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, 0, key.length, hashMapResult); - if (joinResult == JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult == MapJoinHashTableResult.MapJoinResult.MATCH) { // A problem or need different random seed / longer key? assertTrue(false); } for (int v = 0; v < count; v++) { byte[] value = randomByteArrayValueStreams[i].next(); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); } } for (int i = 0; i < largeSize; i++) { - verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStreams[i]); + verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStreams[i], + hashMapResult); } } @@ -177,8 +233,14 @@ public void testLarge() throws Exception { public void testLargeAndExpand() throws Exception { random = new Random(46809); + VectorMapJoinFastHashTableFactory factory = + new VectorMapJoinFastHashTableFactory(HashTableKeyType.MULTI_KEY); + MapJoinHashMapResult hashMapResult = factory.createHashMapResult(); + TestKeyValueWriter kvWriter = new TestKeyValueWriter(); + // Use a large capacity that doesn't require expansion, yet. - VectorMapJoinFastMultiKeyHashMap map = new VectorMapJoinFastMultiKeyHashMap(false, MODERATE_CAPACITY, LOAD_FACTOR, MODERATE_WB_SIZE); + VectorMapJoinFastLongHashMap map = + (VectorMapJoinFastLongHashMap) factory.createHashTable(MODERATE_CAPACITY, LOAD_FACTOR, MODERATE_WB_SIZE, 0); RandomByteArrayStream randomByteArrayKeyStream = new RandomByteArrayStream(random, 10); @@ -188,40 +250,47 @@ public void testLargeAndExpand() throws Exception { randomByteArrayValueStreams[i] = new RandomByteArrayStream(random); int count = generateLargeCount(); byte[] key = randomByteArrayKeyStream.next(); - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, 0, key.length, hashMapResult); - if (joinResult == JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult == MapJoinHashTableResult.MapJoinResult.MATCH) { // A problem or need different random seed / longer key? assertTrue(false); } for (int v = 0; v < count; v++) { byte[] value = randomByteArrayValueStreams[i].next(); - map.putRow(key, value); + kvWriter.setKeyValue(key, value); + map.put(kvWriter); } } for (int i = 0; i < largeSize; i++) { - verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStreams[i]); + verifyHashMapResult(map, randomByteArrayKeyStream.get(i), randomByteArrayValueStreams[i], + hashMapResult); } } - private void verifyHashMapResult(VectorMapJoinFastMultiKeyHashMap map, byte[] key, - RandomByteArrayStream randomByteArrayValueStream) { + private void verifyHashMapResult(MapJoinHashTable map, byte[] key, + RandomByteArrayStream randomByteArrayValueStream, + MapJoinHashMapResult hashMapResult) throws IOException { - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, 0, key.length, hashMapResult); - if (joinResult != JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult != MapJoinHashTableResult.MapJoinResult.MATCH) { assertTrue(false); } CommonFastHashTable.verifyHashMapResult(hashMapResult, randomByteArrayValueStream); } - private void verifyHashMapResult(VectorMapJoinFastMultiKeyHashMap map, byte[] key, - byte[] valueBytes) { + private void verifyHashMapResult(MapJoinHashTable map, byte[] key, + byte[] valueBytes, + MapJoinHashMapResult hashMapResult) throws IOException { - VectorMapJoinHashMapResult hashMapResult = map.createHashMapResult(); - JoinUtil.JoinResult joinResult = map.lookup(key, 0, key.length, hashMapResult); - if (joinResult != JoinUtil.JoinResult.MATCH) { + int hashCode = HashCodeUtil.murmurHash(key, 0, key.length); + map.hashMapLookup(key, 0, key.length, hashCode, hashMapResult); + MapJoinHashTableResult.MapJoinResult lookupResult = hashMapResult.getMapJoinResult(); + if (lookupResult != MapJoinHashTableResult.MapJoinResult.MATCH) { assertTrue(false); } diff --git ql/src/test/queries/clientpositive/vector_char_2.q ql/src/test/queries/clientpositive/vector_char_2.q index f1bb75b..95f3f97 100644 --- ql/src/test/queries/clientpositive/vector_char_2.q +++ ql/src/test/queries/clientpositive/vector_char_2.q @@ -1,6 +1,7 @@ set hive.mapred.mode=nonstrict; set hive.explain.user=false; SET hive.vectorized.execution.enabled=true; +SET hive.vectorized.execution.reducesink.new.enabled=true; drop table char_2; create table char_2 ( diff --git ql/src/test/queries/clientpositive/vector_distinct_1.q ql/src/test/queries/clientpositive/vector_distinct_1.q new file mode 100644 index 0000000..d908d96 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_distinct_1.q @@ -0,0 +1,3 @@ +set hive.vectorized.execution.enabled=true; + +select count(distinct(cint)) from alltypesorc; diff --git ql/src/test/queries/clientpositive/vector_groupby1.q ql/src/test/queries/clientpositive/vector_groupby1.q new file mode 100644 index 0000000..af87180 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby1.q @@ -0,0 +1,75 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25; + +-- +-- Single Long Aggregations +explain +select b, count(*) from vectortab2korc group by b; + +select b, count(*) from vectortab2korc group by b; +select b, min(b) from vectortab2korc group by b; +select b, max(b) from vectortab2korc group by b; +select b, sum(b) from vectortab2korc group by b; +select b, count(b) from vectortab2korc group by b; + + +select i, count(*) from vectortab2korc group by i; +select i, min(i) from vectortab2korc group by i; +select i, max(i) from vectortab2korc group by i; +select i, sum(i) from vectortab2korc group by i; +select i, count(i) from vectortab2korc group by i; + + +select si, count(*) from vectortab2korc group by si; +select si, min(si) from vectortab2korc group by si; +select si, max(si) from vectortab2korc group by si; +select si, sum(si) from vectortab2korc group by si; +select si, count(si) from vectortab2korc group by si; + + +select t, count(*) from vectortab2korc group by t; +select t, min(t) from vectortab2korc group by t; +select t, max(t) from vectortab2korc group by t; +select t, sum(t) from vectortab2korc group by t; +select t, count(t) from vectortab2korc group by t; + diff --git ql/src/test/queries/clientpositive/vector_groupby10.q ql/src/test/queries/clientpositive/vector_groupby10.q new file mode 100644 index 0000000..638e83e --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby10.q @@ -0,0 +1,55 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k; + +-- +-- All Long Aggregations with String key +explain +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s; + +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s; + +select s, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by s; + +select s, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by s; + +select s, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by s; diff --git ql/src/test/queries/clientpositive/vector_groupby11.q ql/src/test/queries/clientpositive/vector_groupby11.q new file mode 100644 index 0000000..aaf3679 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby11.q @@ -0,0 +1,76 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25; + +-- +-- Multiple Long Key Aggregations (not including min, max, sum) +-- +explain +select b,i,count(*) from vectortab2korc group by b,i; + +select b,i,count(*) from vectortab2korc group by b,i; +select b,i,min(b) from vectortab2korc group by b,i; +select b,i,max(b) from vectortab2korc group by b,i; +select b,i,sum(b) from vectortab2korc group by b,i; +select b,i,count(b) from vectortab2korc group by b,i; + + +select i,b,count(*) from vectortab2korc group by i,b; +select i,b,min(i) from vectortab2korc group by i,b; +select i,b,max(i) from vectortab2korc group by i,b; +select i,b,sum(i) from vectortab2korc group by i,b; +select i,b,count(i) from vectortab2korc group by i,b; + + +select si,b,count(*) from vectortab2korc group by si,b; +select si,b,min(si) from vectortab2korc group by si,b; +select si,b,max(si) from vectortab2korc group by si,b; +select si,b,sum(si) from vectortab2korc group by si,b; +select si,b,count(si) from vectortab2korc group by si,b; + + +select t,i,count(*) from vectortab2korc group by t,i; +select t,i,min(t) from vectortab2korc group by t,i; +select t,i,max(t) from vectortab2korc group by t,i; +select t,i,sum(t) from vectortab2korc group by t,i; +select t,i,count(t) from vectortab2korc group by t,i; + diff --git ql/src/test/queries/clientpositive/vector_groupby2.q ql/src/test/queries/clientpositive/vector_groupby2.q new file mode 100644 index 0000000..43e22e7 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby2.q @@ -0,0 +1,55 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k; + +-- +-- All Long Aggregations +explain +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b; + +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b; + +select i, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by i; + +select si, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by si; + +select t, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by t; diff --git ql/src/test/queries/clientpositive/vector_groupby3.q ql/src/test/queries/clientpositive/vector_groupby3.q new file mode 100644 index 0000000..62b0684 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby3.q @@ -0,0 +1,19 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +CREATE TABLE orc_table_1(a INT, b INT) STORED AS ORC; + +insert into table orc_table_1 values(12, 1),(5, 1), (9, 2),(12, 1); + +-- +-- 2nd Column Long Aggregations +explain +select b, max(a) from orc_table_1 group by b; + +select b, min(a) from orc_table_1 group by b; +select b, max(a) from orc_table_1 group by b; +select b, count(a) from orc_table_1 group by b; +select b, sum(a) from orc_table_1 group by b; \ No newline at end of file diff --git ql/src/test/queries/clientpositive/vector_groupby4.q ql/src/test/queries/clientpositive/vector_groupby4.q new file mode 100644 index 0000000..5fbb313 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby4.q @@ -0,0 +1,52 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k; + +-- +-- Many Column Long Aggregations +explain +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b; + +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b; + +select b, max(t), sum(t), min(t), min(si), sum(si), max(si), min(i), sum(i), count(i), max(i), max(b) from vectortab2korc group by b; + diff --git ql/src/test/queries/clientpositive/vector_groupby5.q ql/src/test/queries/clientpositive/vector_groupby5.q new file mode 100644 index 0000000..26d33d8 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby5.q @@ -0,0 +1,61 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25; + +-- +-- Single Double Aggregations +explain +select d, count(*) from vectortab2korc group by d; + +select d, count(*) from vectortab2korc group by d; +select d, min(d) from vectortab2korc group by d; +select d, max(d) from vectortab2korc group by d; +select d, sum(d) from vectortab2korc group by d; +select d, count(d) from vectortab2korc group by d; + + +select f, count(*) from vectortab2korc group by f; +select f, min(f) from vectortab2korc group by f; +select f, max(f) from vectortab2korc group by f; +select f, sum(f) from vectortab2korc group by f; +select f, count(f) from vectortab2korc group by f; + diff --git ql/src/test/queries/clientpositive/vector_groupby6.q ql/src/test/queries/clientpositive/vector_groupby6.q new file mode 100644 index 0000000..77521ed --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby6.q @@ -0,0 +1,52 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k; + +-- +-- All Double Aggregations +explain +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d; + +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d; + +select f, count(*), min(f), max(f), sum(f), count(f) from vectortab2korc group by f; + diff --git ql/src/test/queries/clientpositive/vector_groupby7.q ql/src/test/queries/clientpositive/vector_groupby7.q new file mode 100644 index 0000000..9803430 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby7.q @@ -0,0 +1,56 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k; + +-- +-- Many Column Double Aggregations +explain +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b; + +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b; + +select b, max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b; + + diff --git ql/src/test/queries/clientpositive/vector_groupby8.q ql/src/test/queries/clientpositive/vector_groupby8.q new file mode 100644 index 0000000..edb3ae9 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby8.q @@ -0,0 +1,56 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k; + +-- +-- Many Column Long, Double Aggregations +explain +select b, min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b; + +select b, max(d2), min(si), sum(d2), min(f), count(t), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b; + +select b, min(t), max(t), sum(t), count(t), max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b; + + diff --git ql/src/test/queries/clientpositive/vector_groupby9.q ql/src/test/queries/clientpositive/vector_groupby9.q new file mode 100644 index 0000000..cefed89 --- /dev/null +++ ql/src/test/queries/clientpositive/vector_groupby9.q @@ -0,0 +1,51 @@ +set hive.explain.user=false; +set hive.cli.print.header=true; +SET hive.vectorized.execution.enabled=true; + +-- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE; + +LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k; + +create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC; + +INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k; + +-- +-- Single String Aggregations (not including min, max, sum) +explain +select s, count(*) from vectortab2korc group by s; + +select s, count(*) from vectortab2korc group by s; +select s, count(b) from vectortab2korc group by s; + diff --git ql/src/test/queries/clientpositive/vector_outer_join0.q ql/src/test/queries/clientpositive/vector_outer_join0.q index dce3a1b..19142b0 100644 --- ql/src/test/queries/clientpositive/vector_outer_join0.q +++ ql/src/test/queries/clientpositive/vector_outer_join0.q @@ -1,4 +1,5 @@ set hive.mapred.mode=nonstrict; +set hive.cli.print.header=true; set hive.explain.user=false; SET hive.vectorized.execution.enabled=true; SET hive.auto.convert.join=true; diff --git ql/src/test/queries/clientpositive/vector_outer_join3.q ql/src/test/queries/clientpositive/vector_outer_join3.q index e5fc0a9..b34d0f3 100644 --- ql/src/test/queries/clientpositive/vector_outer_join3.q +++ ql/src/test/queries/clientpositive/vector_outer_join3.q @@ -1,3 +1,4 @@ +set hive.cli.print.header=true; set hive.explain.user=false; SET hive.vectorized.execution.enabled=true; SET hive.auto.convert.join=true; diff --git ql/src/test/queries/clientpositive/vector_outer_join5.q ql/src/test/queries/clientpositive/vector_outer_join5.q index 18b9ab4..ae5b53e 100644 --- ql/src/test/queries/clientpositive/vector_outer_join5.q +++ ql/src/test/queries/clientpositive/vector_outer_join5.q @@ -1,7 +1,7 @@ set hive.mapred.mode=nonstrict; set hive.explain.user=false; -SET hive.vectorized.execution.enabled=true; -SET hive.vectorized.execution.mapjoin.native.enabled=true; +SET hive.vectorized.execution.enabled=false; +SET hive.vectorized.execution.mapjoin.native.enabled=false; set hive.auto.convert.join=true; set hive.auto.convert.join.noconditionaltask=true; set hive.auto.convert.join.noconditionaltask.size=10000; diff --git ql/src/test/results/clientpositive/stats_ppr_all.q.out ql/src/test/results/clientpositive/stats_ppr_all.q.out index c63c5b7..3717710 100644 --- ql/src/test/results/clientpositive/stats_ppr_all.q.out +++ ql/src/test/results/clientpositive/stats_ppr_all.q.out @@ -74,29 +74,29 @@ STAGE PLANS: Map Operator Tree: TableScan alias: ss - Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: NONE Select Operator expressions: order_amount (type: float) outputColumnNames: order_amount - Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: NONE Group By Operator aggregations: sum(order_amount) mode: hash outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE Reduce Output Operator sort order: - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: double) Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) mode: mergepartial outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE File Output Operator compressed: false - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat @@ -173,29 +173,29 @@ STAGE PLANS: Map Operator Tree: TableScan alias: ss - Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: NONE Select Operator expressions: order_amount (type: float) outputColumnNames: order_amount - Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: NONE Group By Operator aggregations: sum(order_amount) mode: hash outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE Reduce Output Operator sort order: - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: double) Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) mode: mergepartial outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE File Output Operator compressed: false - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat @@ -221,32 +221,32 @@ STAGE PLANS: Map Operator Tree: TableScan alias: ss - Statistics: Num rows: 2 Data size: 24 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 2 Data size: 24 Basic stats: COMPLETE Column stats: PARTIAL Filter Operator predicate: (UDFToDouble(((201500 + (month * 10)) + day)) > 201511.0) (type: boolean) - Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL Select Operator expressions: order_amount (type: float) outputColumnNames: order_amount - Statistics: Num rows: 1 Data size: 12 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL Group By Operator aggregations: sum(order_amount) mode: hash outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL Reduce Output Operator sort order: - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL value expressions: _col0 (type: double) Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) mode: mergepartial outputColumnNames: _col0 - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL File Output Operator compressed: false - Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: PARTIAL table: input format: org.apache.hadoop.mapred.TextInputFormat output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat diff --git ql/src/test/results/clientpositive/tez/auto_join30.q.out ql/src/test/results/clientpositive/tez/auto_join30.q.out index 8c20985..e88db4a 100644 --- ql/src/test/results/clientpositive/tez/auto_join30.q.out +++ ql/src/test/results/clientpositive/tez/auto_join30.q.out @@ -588,7 +588,7 @@ select sum(hash(Y.key,Y.value)) POSTHOOK: type: QUERY POSTHOOK: Input: default@src #### A masked pattern was here #### -348019368476 +443482983923 PREHOOK: query: explain FROM (SELECT src.* FROM src sort by key) x diff --git ql/src/test/results/clientpositive/tez/auto_sortmerge_join_11.q.out ql/src/test/results/clientpositive/tez/auto_sortmerge_join_11.q.out index f73ac95..ffd8471 100644 --- ql/src/test/results/clientpositive/tez/auto_sortmerge_join_11.q.out +++ ql/src/test/results/clientpositive/tez/auto_sortmerge_join_11.q.out @@ -1489,4 +1489,4 @@ POSTHOOK: Input: default@bucket_big@ds=2008-04-09 POSTHOOK: Input: default@bucket_small POSTHOOK: Input: default@bucket_small@ds=2008-04-08 #### A masked pattern was here #### -180 +244 diff --git ql/src/test/results/clientpositive/tez/auto_sortmerge_join_12.q.out ql/src/test/results/clientpositive/tez/auto_sortmerge_join_12.q.out index f005a0f..cc4df4c 100644 --- ql/src/test/results/clientpositive/tez/auto_sortmerge_join_12.q.out +++ ql/src/test/results/clientpositive/tez/auto_sortmerge_join_12.q.out @@ -640,4 +640,4 @@ POSTHOOK: Input: default@bucket_medium@ds=2008-04-08 POSTHOOK: Input: default@bucket_small POSTHOOK: Input: default@bucket_small@ds=2008-04-08 #### A masked pattern was here #### -570 +630 diff --git ql/src/test/results/clientpositive/tez/auto_sortmerge_join_13.q.out ql/src/test/results/clientpositive/tez/auto_sortmerge_join_13.q.out index c0f99cc..182723d 100644 --- ql/src/test/results/clientpositive/tez/auto_sortmerge_join_13.q.out +++ ql/src/test/results/clientpositive/tez/auto_sortmerge_join_13.q.out @@ -443,6 +443,7 @@ POSTHOOK: Input: default@dest1 5 5 5 5 8 8 +8 8 9 9 PREHOOK: query: select * from dest2 PREHOOK: type: QUERY @@ -472,6 +473,7 @@ val_5 val_5 val_5 val_5 val_5 val_5 val_5 val_5 +val_8 val_4 val_8 val_8 val_9 val_9 PREHOOK: query: -- A SMB join followed by a mutli-insert @@ -653,6 +655,7 @@ POSTHOOK: Input: default@dest1 5 5 5 5 8 8 +8 8 9 9 PREHOOK: query: select * from dest2 PREHOOK: type: QUERY @@ -682,5 +685,6 @@ val_5 val_5 val_5 val_5 val_5 val_5 val_5 val_5 +val_8 val_4 val_8 val_8 val_9 val_9 diff --git ql/src/test/results/clientpositive/tez/bucket_map_join_tez1.q.out ql/src/test/results/clientpositive/tez/bucket_map_join_tez1.q.out index 2447f19..827f3c1 100644 --- ql/src/test/results/clientpositive/tez/bucket_map_join_tez1.q.out +++ ql/src/test/results/clientpositive/tez/bucket_map_join_tez1.q.out @@ -312,7 +312,7 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -242 +248 PREHOOK: query: -- one side is really bucketed. srcbucket_mapjoin is not really a bucketed table. -- In this case the sub-query is chosen as the big table. explain diff --git ql/src/test/results/clientpositive/tez/dynpart_sort_opt_vectorization.q.out ql/src/test/results/clientpositive/tez/dynpart_sort_opt_vectorization.q.out index 2f6753a..3517b17 100644 --- ql/src/test/results/clientpositive/tez/dynpart_sort_opt_vectorization.q.out +++ ql/src/test/results/clientpositive/tez/dynpart_sort_opt_vectorization.q.out @@ -190,7 +190,6 @@ STAGE PLANS: value expressions: _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint), VALUE._col0 (type: int), VALUE._col1 (type: bigint), VALUE._col2 (type: float), VALUE._col3 (type: tinyint) @@ -203,7 +202,6 @@ STAGE PLANS: Statistics: Num rows: 1048 Data size: 310873 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -277,7 +275,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -293,7 +290,6 @@ STAGE PLANS: Statistics: Num rows: 10 Data size: 2960 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -364,7 +360,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint), '_bucket_number' (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint), VALUE._bucket_number (type: string) @@ -434,7 +429,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint), '_bucket_number' (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint), VALUE._bucket_number (type: string) @@ -574,7 +568,6 @@ STAGE PLANS: value expressions: _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint), VALUE._col0 (type: int), VALUE._col1 (type: bigint), VALUE._col2 (type: float), VALUE._col3 (type: tinyint) @@ -587,7 +580,6 @@ STAGE PLANS: Statistics: Num rows: 1048 Data size: 310873 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -661,7 +653,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -677,7 +668,6 @@ STAGE PLANS: Statistics: Num rows: 10 Data size: 2960 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -748,7 +738,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint), '_bucket_number' (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint), VALUE._bucket_number (type: string) @@ -818,7 +807,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint), '_bucket_number' (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint), VALUE._bucket_number (type: string) @@ -1359,7 +1347,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), KEY.reducesinkkey0 (type: int), VALUE._col1 (type: bigint), VALUE._col2 (type: float), VALUE._col3 (type: tinyint) @@ -1430,7 +1417,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), KEY.reducesinkkey0 (type: int), VALUE._col1 (type: bigint), VALUE._col2 (type: float), VALUE._col3 (type: tinyint) @@ -1443,7 +1429,6 @@ STAGE PLANS: Statistics: Num rows: 1048 Data size: 310873 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -1512,7 +1497,6 @@ STAGE PLANS: value expressions: _col0 (type: tinyint), _col1 (type: smallint), _col3 (type: bigint), _col4 (type: float) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: tinyint), VALUE._col1 (type: smallint), KEY.reducesinkkey0 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float) @@ -1535,7 +1519,6 @@ STAGE PLANS: Statistics: Num rows: 10 Data size: 2960 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint) @@ -1606,7 +1589,6 @@ STAGE PLANS: Statistics: Num rows: 1048 Data size: 310873 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: tinyint), KEY._col1 (type: smallint), KEY._col2 (type: int), KEY._col3 (type: bigint), KEY._col4 (type: float) @@ -1684,7 +1666,6 @@ STAGE PLANS: Statistics: Num rows: 1048 Data size: 310873 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: tinyint), KEY._col1 (type: smallint), KEY._col2 (type: int), KEY._col3 (type: bigint), KEY._col4 (type: float) @@ -2082,7 +2063,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col4 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), KEY.reducesinkkey0 (type: float), VALUE._col3 (type: tinyint) @@ -2152,7 +2132,6 @@ STAGE PLANS: value expressions: _col0 (type: smallint), _col1 (type: int), _col2 (type: bigint), _col3 (type: float), _col4 (type: tinyint), '_bucket_number' (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), VALUE._col1 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: tinyint), VALUE._bucket_number (type: string) @@ -2366,7 +2345,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2581,7 +2559,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/dynpart_sort_optimization2.q.out ql/src/test/results/clientpositive/tez/dynpart_sort_optimization2.q.out index 346e52f..94ed574 100644 --- ql/src/test/results/clientpositive/tez/dynpart_sort_optimization2.q.out +++ ql/src/test/results/clientpositive/tez/dynpart_sort_optimization2.q.out @@ -1113,7 +1113,6 @@ STAGE PLANS: Statistics: Num rows: 2 Data size: 24 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int), KEY._col1 (type: float), KEY._col2 (type: float) @@ -1352,7 +1351,6 @@ STAGE PLANS: value expressions: _col0 (type: float), _col1 (type: float), _col2 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: float), VALUE._col1 (type: float), VALUE._col2 (type: int) @@ -1621,7 +1619,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE value expressions: _col2 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1748,7 +1745,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE value expressions: _col2 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/explainuser_3.q.out ql/src/test/results/clientpositive/tez/explainuser_3.q.out index 978cf23..64203d6 100644 --- ql/src/test/results/clientpositive/tez/explainuser_3.q.out +++ ql/src/test/results/clientpositive/tez/explainuser_3.q.out @@ -29,26 +29,26 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized - File Output Operator [FS_8] + Reducer 2 + File Output Operator [FS_4] compressed:false - Statistics:Num rows: 10 Data size: 1704 Basic stats: COMPLETE Column stats: NONE + Statistics:Num rows: 10 Data size: 1730 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Select Operator [OP_7] + Select Operator [SEL_3] | outputColumnNames:["_col0","_col1"] - | Statistics:Num rows: 10 Data size: 1704 Basic stats: COMPLETE Column stats: NONE + | Statistics:Num rows: 10 Data size: 1730 Basic stats: COMPLETE Column stats: NONE |<-Map 1 [SIMPLE_EDGE] vectorized Reduce Output Operator [RS_6] key expressions:_col0 (type: int), _col1 (type: string) sort order:++ - Statistics:Num rows: 10 Data size: 1704 Basic stats: COMPLETE Column stats: NONE + Statistics:Num rows: 10 Data size: 1730 Basic stats: COMPLETE Column stats: NONE Select Operator [OP_5] outputColumnNames:["_col0","_col1"] - Statistics:Num rows: 10 Data size: 1704 Basic stats: COMPLETE Column stats: NONE + Statistics:Num rows: 10 Data size: 1730 Basic stats: COMPLETE Column stats: NONE TableScan [TS_0] ACID table:true alias:acid_vectorized - Statistics:Num rows: 10 Data size: 1704 Basic stats: COMPLETE Column stats: NONE + Statistics:Num rows: 10 Data size: 1730 Basic stats: COMPLETE Column stats: NONE PREHOOK: query: explain select key, value FROM srcpart LATERAL VIEW explode(array(1,2,3)) myTable AS myCol @@ -500,15 +500,15 @@ Stage-0 Fetch Operator limit:5 Stage-1 - Reducer 2 vectorized - File Output Operator [FS_8] + Reducer 2 + File Output Operator [FS_5] compressed:false Statistics:Num rows: 5 Data size: 50 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Limit [LIM_7] + Limit [LIM_4] Number of rows:5 Statistics:Num rows: 5 Data size: 50 Basic stats: COMPLETE Column stats: NONE - Select Operator [OP_6] + Select Operator [SEL_3] | outputColumnNames:["_col0","_col1"] | Statistics:Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE |<-Map 1 [SIMPLE_EDGE] diff --git ql/src/test/results/clientpositive/tez/hybridgrace_hashjoin_2.q.out ql/src/test/results/clientpositive/tez/hybridgrace_hashjoin_2.q.out index 737bd1d..02d9075 100644 --- ql/src/test/results/clientpositive/tez/hybridgrace_hashjoin_2.q.out +++ ql/src/test/results/clientpositive/tez/hybridgrace_hashjoin_2.q.out @@ -268,7 +268,7 @@ POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 #### A masked pattern was here #### -428 +488 PREHOOK: query: -- 4-way mapjoin (1 big table, 3 small tables) SELECT 1 PREHOOK: type: QUERY @@ -1046,8 +1046,8 @@ POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 #### A masked pattern was here #### -428 -452 +488 +536 PREHOOK: query: -- A chain of 2 sets of 3-way mapjoin under the same task SELECT 1 PREHOOK: type: QUERY @@ -1426,4 +1426,4 @@ POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 #### A masked pattern was here #### -18256 +19280 diff --git ql/src/test/results/clientpositive/tez/mapjoin_mapjoin.q.out ql/src/test/results/clientpositive/tez/mapjoin_mapjoin.q.out index f6e652d..58ed59b 100644 --- ql/src/test/results/clientpositive/tez/mapjoin_mapjoin.q.out +++ ql/src/test/results/clientpositive/tez/mapjoin_mapjoin.q.out @@ -808,5 +808,5 @@ POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 #### A masked pattern was here #### -5308 -5308 +15119 +15234 diff --git ql/src/test/results/clientpositive/tez/mergejoin.q.out ql/src/test/results/clientpositive/tez/mergejoin.q.out index 34e4da3..0f98b68 100644 --- ql/src/test/results/clientpositive/tez/mergejoin.q.out +++ ql/src/test/results/clientpositive/tez/mergejoin.q.out @@ -315,7 +315,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1393,7 +1392,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1500,7 +1498,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1607,7 +1604,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1753,7 +1749,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1864,7 +1859,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2036,7 +2030,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2132,7 +2125,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2276,7 +2268,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2450,7 +2441,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2542,7 +2532,6 @@ STAGE PLANS: Statistics: Num rows: 250 Data size: 23500 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) @@ -2572,7 +2561,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2587,7 +2575,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) diff --git ql/src/test/results/clientpositive/tez/tez_bmj_schema_evolution.q.out ql/src/test/results/clientpositive/tez/tez_bmj_schema_evolution.q.out index d7da700..b977451 100644 --- ql/src/test/results/clientpositive/tez/tez_bmj_schema_evolution.q.out +++ ql/src/test/results/clientpositive/tez/tez_bmj_schema_evolution.q.out @@ -179,10 +179,40 @@ POSTHOOK: Input: default@test@p=2 0 val_0 0 val_0 0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +0 val_0 +2 val_2 +2 val_2 +2 val_2 2 val_2 2 val_2 +2 val_2 +4 val_4 +4 val_4 +4 val_4 4 val_4 4 val_4 +4 val_4 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 +5 val_5 5 val_5 5 val_5 5 val_5 @@ -203,12 +233,35 @@ POSTHOOK: Input: default@test@p=2 5 val_5 8 val_8 8 val_8 +8 val_8 +8 val_8 +8 val_8 +8 val_8 +8 val_8 +8 val_8 +9 val_9 +9 val_9 9 val_9 9 val_9 +9 val_9 +9 val_9 +10 val_10 +10 val_10 +10 val_10 +10 val_10 +10 val_10 10 val_10 10 val_10 +10 val_10 +11 val_11 11 val_11 11 val_11 +11 val_11 +12 val_12 +12 val_12 +12 val_12 +12 val_12 +12 val_12 12 val_12 12 val_12 12 val_12 @@ -217,6 +270,13 @@ POSTHOOK: Input: default@test@p=2 12 val_12 12 val_12 12 val_12 +12 val_12 +15 val_15 +15 val_15 +15 val_15 +15 val_15 +15 val_15 +15 val_15 15 val_15 15 val_15 15 val_15 @@ -227,6 +287,14 @@ POSTHOOK: Input: default@test@p=2 15 val_15 17 val_17 17 val_17 +17 val_17 +17 val_17 +17 val_17 +17 val_17 +18 val_18 +18 val_18 +18 val_18 +18 val_18 18 val_18 18 val_18 18 val_18 @@ -235,10 +303,34 @@ POSTHOOK: Input: default@test@p=2 18 val_18 18 val_18 18 val_18 +18 val_18 +18 val_18 +18 val_18 +18 val_18 +18 val_18 +18 val_18 +19 val_19 19 val_19 19 val_19 +19 val_19 +20 val_20 +20 val_20 +20 val_20 20 val_20 20 val_20 +20 val_20 +20 val_20 +20 val_20 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 +24 val_24 24 val_24 24 val_24 24 val_24 @@ -259,12 +351,34 @@ POSTHOOK: Input: default@test@p=2 27 val_27 28 val_28 28 val_28 +28 val_28 +28 val_28 +28 val_28 +28 val_28 +28 val_28 +28 val_28 30 val_30 30 val_30 +30 val_30 +30 val_30 +30 val_30 +30 val_30 +30 val_30 +30 val_30 +33 val_33 33 val_33 33 val_33 +33 val_33 +34 val_34 34 val_34 34 val_34 +34 val_34 +35 val_35 +35 val_35 +35 val_35 +35 val_35 +35 val_35 +35 val_35 35 val_35 35 val_35 35 val_35 @@ -293,6 +407,12 @@ POSTHOOK: Input: default@test@p=2 37 val_37 41 val_41 41 val_41 +41 val_41 +41 val_41 +42 val_42 +42 val_42 +42 val_42 +42 val_42 42 val_42 42 val_42 42 val_42 @@ -301,12 +421,42 @@ POSTHOOK: Input: default@test@p=2 42 val_42 42 val_42 42 val_42 +42 val_42 +42 val_42 +42 val_42 +42 val_42 +42 val_42 +42 val_42 +43 val_43 +43 val_43 43 val_43 43 val_43 +43 val_43 +43 val_43 +43 val_43 +43 val_43 +44 val_44 +44 val_44 +44 val_44 44 val_44 44 val_44 +44 val_44 +47 val_47 47 val_47 47 val_47 +47 val_47 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 +51 val_51 51 val_51 51 val_51 51 val_51 @@ -315,12 +465,27 @@ POSTHOOK: Input: default@test@p=2 51 val_51 51 val_51 51 val_51 +51 val_51 +51 val_51 +53 val_53 +53 val_53 53 val_53 53 val_53 54 val_54 54 val_54 +54 val_54 +54 val_54 +54 val_54 +54 val_54 57 val_57 57 val_57 +57 val_57 +57 val_57 +57 val_57 +57 val_57 +58 val_58 +58 val_58 +58 val_58 58 val_58 58 val_58 58 val_58 @@ -329,8 +494,27 @@ POSTHOOK: Input: default@test@p=2 58 val_58 58 val_58 58 val_58 +58 val_58 +58 val_58 +58 val_58 +58 val_58 +58 val_58 +64 val_64 +64 val_64 +64 val_64 64 val_64 64 val_64 +64 val_64 +64 val_64 +64 val_64 +65 val_65 +65 val_65 +65 val_65 +65 val_65 +65 val_65 +65 val_65 +65 val_65 +65 val_65 65 val_65 65 val_65 66 val_66 @@ -343,8 +527,34 @@ POSTHOOK: Input: default@test@p=2 67 val_67 67 val_67 67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +67 val_67 +69 val_69 69 val_69 69 val_69 +69 val_69 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 +70 val_70 70 val_70 70 val_70 70 val_70 @@ -363,6 +573,14 @@ POSTHOOK: Input: default@test@p=2 70 val_70 70 val_70 70 val_70 +70 val_70 +70 val_70 +72 val_72 +72 val_72 +72 val_72 +72 val_72 +72 val_72 +72 val_72 72 val_72 72 val_72 72 val_72 @@ -371,8 +589,29 @@ POSTHOOK: Input: default@test@p=2 72 val_72 72 val_72 72 val_72 +72 val_72 +72 val_72 +72 val_72 +72 val_72 +74 val_74 +74 val_74 +74 val_74 74 val_74 74 val_74 +74 val_74 +74 val_74 +74 val_74 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 +76 val_76 76 val_76 76 val_76 76 val_76 @@ -381,12 +620,35 @@ POSTHOOK: Input: default@test@p=2 76 val_76 76 val_76 76 val_76 +76 val_76 +77 val_77 +77 val_77 +77 val_77 +77 val_77 +77 val_77 +77 val_77 +77 val_77 77 val_77 77 val_77 +77 val_77 +78 val_78 +78 val_78 +78 val_78 +78 val_78 +78 val_78 +78 val_78 78 val_78 78 val_78 80 val_80 80 val_80 +80 val_80 +80 val_80 +80 val_80 +80 val_80 +82 val_82 +82 val_82 +82 val_82 +82 val_82 82 val_82 82 val_82 83 val_83 @@ -397,6 +659,28 @@ POSTHOOK: Input: default@test@p=2 83 val_83 83 val_83 83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +83 val_83 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 +84 val_84 84 val_84 84 val_84 84 val_84 @@ -405,12 +689,42 @@ POSTHOOK: Input: default@test@p=2 84 val_84 84 val_84 84 val_84 +84 val_84 +84 val_84 +85 val_85 +85 val_85 +85 val_85 +85 val_85 +85 val_85 +85 val_85 85 val_85 85 val_85 +85 val_85 +85 val_85 +86 val_86 +86 val_86 +86 val_86 +86 val_86 +86 val_86 86 val_86 86 val_86 +86 val_86 +87 val_87 +87 val_87 +87 val_87 +87 val_87 +87 val_87 +87 val_87 +87 val_87 87 val_87 87 val_87 +87 val_87 +90 val_90 +90 val_90 +90 val_90 +90 val_90 +90 val_90 +90 val_90 90 val_90 90 val_90 90 val_90 @@ -429,6 +743,14 @@ POSTHOOK: Input: default@test@p=2 90 val_90 90 val_90 90 val_90 +90 val_90 +90 val_90 +90 val_90 +90 val_90 +92 val_92 +92 val_92 +92 val_92 +92 val_92 92 val_92 92 val_92 95 val_95 @@ -439,6 +761,14 @@ POSTHOOK: Input: default@test@p=2 95 val_95 95 val_95 95 val_95 +95 val_95 +95 val_95 +95 val_95 +95 val_95 +95 val_95 +95 val_95 +95 val_95 +95 val_95 96 val_96 96 val_96 97 val_97 @@ -449,6 +779,20 @@ POSTHOOK: Input: default@test@p=2 97 val_97 97 val_97 97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +97 val_97 +98 val_98 +98 val_98 98 val_98 98 val_98 98 val_98 @@ -457,6 +801,21 @@ POSTHOOK: Input: default@test@p=2 98 val_98 98 val_98 98 val_98 +98 val_98 +98 val_98 +98 val_98 +98 val_98 +98 val_98 +98 val_98 +100 val_100 +100 val_100 +100 val_100 +100 val_100 +100 val_100 +100 val_100 +100 val_100 +100 val_100 +100 val_100 100 val_100 100 val_100 100 val_100 @@ -465,6 +824,14 @@ POSTHOOK: Input: default@test@p=2 100 val_100 100 val_100 100 val_100 +100 val_100 +103 val_103 +103 val_103 +103 val_103 +103 val_103 +103 val_103 +103 val_103 +103 val_103 103 val_103 103 val_103 103 val_103 @@ -473,6 +840,17 @@ POSTHOOK: Input: default@test@p=2 103 val_103 103 val_103 103 val_103 +103 val_103 +103 val_103 +103 val_103 +104 val_104 +104 val_104 +104 val_104 +104 val_104 +104 val_104 +104 val_104 +104 val_104 +104 val_104 104 val_104 104 val_104 104 val_104 @@ -483,6 +861,8 @@ POSTHOOK: Input: default@test@p=2 104 val_104 105 val_105 105 val_105 +105 val_105 +105 val_105 111 val_111 111 val_111 113 val_113 @@ -493,10 +873,34 @@ POSTHOOK: Input: default@test@p=2 113 val_113 113 val_113 113 val_113 +113 val_113 +113 val_113 +113 val_113 +113 val_113 +113 val_113 +113 val_113 +114 val_114 +114 val_114 +114 val_114 114 val_114 114 val_114 +114 val_114 +116 val_116 +116 val_116 +116 val_116 116 val_116 116 val_116 +116 val_116 +116 val_116 +116 val_116 +118 val_118 +118 val_118 +118 val_118 +118 val_118 +118 val_118 +118 val_118 +118 val_118 +118 val_118 118 val_118 118 val_118 118 val_118 @@ -505,6 +909,14 @@ POSTHOOK: Input: default@test@p=2 118 val_118 118 val_118 118 val_118 +118 val_118 +118 val_118 +119 val_119 +119 val_119 +119 val_119 +119 val_119 +119 val_119 +119 val_119 119 val_119 119 val_119 119 val_119 @@ -523,6 +935,13 @@ POSTHOOK: Input: default@test@p=2 119 val_119 119 val_119 119 val_119 +119 val_119 +119 val_119 +119 val_119 +119 val_119 +119 val_119 +119 val_119 +120 val_120 120 val_120 120 val_120 120 val_120 @@ -531,6 +950,21 @@ POSTHOOK: Input: default@test@p=2 120 val_120 120 val_120 120 val_120 +120 val_120 +120 val_120 +120 val_120 +120 val_120 +120 val_120 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 125 val_125 125 val_125 125 val_125 @@ -539,8 +973,23 @@ POSTHOOK: Input: default@test@p=2 125 val_125 125 val_125 125 val_125 +125 val_125 +125 val_125 +125 val_125 +125 val_125 +126 val_126 +126 val_126 +126 val_126 +126 val_126 126 val_126 126 val_126 +126 val_126 +126 val_126 +128 val_128 +128 val_128 +128 val_128 +128 val_128 +128 val_128 128 val_128 128 val_128 128 val_128 @@ -559,6 +1008,14 @@ POSTHOOK: Input: default@test@p=2 128 val_128 128 val_128 128 val_128 +128 val_128 +129 val_129 +129 val_129 +129 val_129 +129 val_129 +129 val_129 +129 val_129 +129 val_129 129 val_129 129 val_129 129 val_129 @@ -567,10 +1024,31 @@ POSTHOOK: Input: default@test@p=2 129 val_129 129 val_129 129 val_129 +129 val_129 +129 val_129 +129 val_129 +131 val_131 +131 val_131 +131 val_131 +131 val_131 131 val_131 131 val_131 +131 val_131 +131 val_131 +133 val_133 +133 val_133 133 val_133 133 val_133 +133 val_133 +133 val_133 +134 val_134 +134 val_134 +134 val_134 +134 val_134 +134 val_134 +134 val_134 +134 val_134 +134 val_134 134 val_134 134 val_134 134 val_134 @@ -581,6 +1059,22 @@ POSTHOOK: Input: default@test@p=2 134 val_134 136 val_136 136 val_136 +136 val_136 +136 val_136 +136 val_136 +136 val_136 +136 val_136 +136 val_136 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 137 val_137 137 val_137 137 val_137 @@ -589,6 +1083,30 @@ POSTHOOK: Input: default@test@p=2 137 val_137 137 val_137 137 val_137 +137 val_137 +137 val_137 +137 val_137 +137 val_137 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 +138 val_138 138 val_138 138 val_138 138 val_138 @@ -623,8 +1141,16 @@ POSTHOOK: Input: default@test@p=2 138 val_138 143 val_143 143 val_143 +143 val_143 +143 val_143 +145 val_145 +145 val_145 145 val_145 145 val_145 +145 val_145 +145 val_145 +146 val_146 +146 val_146 146 val_146 146 val_146 146 val_146 @@ -633,6 +1159,22 @@ POSTHOOK: Input: default@test@p=2 146 val_146 146 val_146 146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +146 val_146 +149 val_149 +149 val_149 +149 val_149 +149 val_149 +149 val_149 +149 val_149 149 val_149 149 val_149 149 val_149 @@ -643,6 +1185,22 @@ POSTHOOK: Input: default@test@p=2 149 val_149 150 val_150 150 val_150 +150 val_150 +150 val_150 +150 val_150 +150 val_150 +150 val_150 +150 val_150 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 +152 val_152 152 val_152 152 val_152 152 val_152 @@ -651,22 +1209,62 @@ POSTHOOK: Input: default@test@p=2 152 val_152 152 val_152 152 val_152 +152 val_152 +152 val_152 +153 val_153 +153 val_153 153 val_153 153 val_153 +153 val_153 +153 val_153 +155 val_155 +155 val_155 155 val_155 155 val_155 +155 val_155 +155 val_155 +156 val_156 +156 val_156 156 val_156 156 val_156 +156 val_156 +156 val_156 +157 val_157 +157 val_157 157 val_157 157 val_157 +157 val_157 +157 val_157 +158 val_158 +158 val_158 158 val_158 158 val_158 +158 val_158 +158 val_158 +160 val_160 +160 val_160 160 val_160 160 val_160 +160 val_160 +160 val_160 +160 val_160 +160 val_160 +162 val_162 +162 val_162 +162 val_162 +162 val_162 162 val_162 162 val_162 163 val_163 163 val_163 +163 val_163 +163 val_163 +163 val_163 +163 val_163 +164 val_164 +164 val_164 +164 val_164 +164 val_164 164 val_164 164 val_164 164 val_164 @@ -675,6 +1273,14 @@ POSTHOOK: Input: default@test@p=2 164 val_164 164 val_164 164 val_164 +164 val_164 +164 val_164 +164 val_164 +164 val_164 +165 val_165 +165 val_165 +165 val_165 +165 val_165 165 val_165 165 val_165 165 val_165 @@ -683,6 +1289,14 @@ POSTHOOK: Input: default@test@p=2 165 val_165 165 val_165 165 val_165 +165 val_165 +165 val_165 +166 val_166 +166 val_166 +166 val_166 +166 val_166 +166 val_166 +166 val_166 166 val_166 166 val_166 167 val_167 @@ -703,6 +1317,18 @@ POSTHOOK: Input: default@test@p=2 167 val_167 167 val_167 167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +167 val_167 +168 val_168 +168 val_168 168 val_168 168 val_168 169 val_169 @@ -739,6 +1365,12 @@ POSTHOOK: Input: default@test@p=2 169 val_169 170 val_170 170 val_170 +170 val_170 +170 val_170 +170 val_170 +170 val_170 +170 val_170 +170 val_170 172 val_172 172 val_172 172 val_172 @@ -755,6 +1387,20 @@ POSTHOOK: Input: default@test@p=2 174 val_174 174 val_174 174 val_174 +174 val_174 +174 val_174 +174 val_174 +174 val_174 +174 val_174 +174 val_174 +174 val_174 +174 val_174 +175 val_175 +175 val_175 +175 val_175 +175 val_175 +175 val_175 +175 val_175 175 val_175 175 val_175 175 val_175 @@ -771,10 +1417,34 @@ POSTHOOK: Input: default@test@p=2 176 val_176 176 val_176 176 val_176 +176 val_176 +176 val_176 +176 val_176 +176 val_176 177 val_177 177 val_177 +177 val_177 +177 val_177 +178 val_178 +178 val_178 178 val_178 178 val_178 +178 val_178 +178 val_178 +178 val_178 +178 val_178 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 +179 val_179 179 val_179 179 val_179 179 val_179 @@ -783,14 +1453,46 @@ POSTHOOK: Input: default@test@p=2 179 val_179 179 val_179 179 val_179 +179 val_179 +179 val_179 +180 val_180 +180 val_180 180 val_180 180 val_180 181 val_181 181 val_181 +181 val_181 +181 val_181 +181 val_181 +181 val_181 +181 val_181 +181 val_181 +181 val_181 +181 val_181 +183 val_183 +183 val_183 +183 val_183 +183 val_183 183 val_183 183 val_183 186 val_186 186 val_186 +186 val_186 +186 val_186 +186 val_186 +186 val_186 +186 val_186 +186 val_186 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 187 val_187 187 val_187 187 val_187 @@ -809,6 +1511,20 @@ POSTHOOK: Input: default@test@p=2 187 val_187 187 val_187 187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +187 val_187 +189 val_189 +189 val_189 +189 val_189 +189 val_189 189 val_189 189 val_189 190 val_190 @@ -821,8 +1537,26 @@ POSTHOOK: Input: default@test@p=2 191 val_191 191 val_191 191 val_191 +191 val_191 +191 val_191 +191 val_191 +191 val_191 +191 val_191 +191 val_191 +191 val_191 +191 val_191 +192 val_192 +192 val_192 192 val_192 192 val_192 +192 val_192 +192 val_192 +192 val_192 +192 val_192 +193 val_193 +193 val_193 +193 val_193 +193 val_193 193 val_193 193 val_193 193 val_193 @@ -841,8 +1575,24 @@ POSTHOOK: Input: default@test@p=2 193 val_193 193 val_193 193 val_193 +193 val_193 +193 val_193 +193 val_193 +193 val_193 +193 val_193 +193 val_193 +194 val_194 +194 val_194 194 val_194 194 val_194 +194 val_194 +194 val_194 +194 val_194 +194 val_194 +195 val_195 +195 val_195 +195 val_195 +195 val_195 195 val_195 195 val_195 195 val_195 @@ -851,8 +1601,24 @@ POSTHOOK: Input: default@test@p=2 195 val_195 195 val_195 195 val_195 +195 val_195 +195 val_195 +196 val_196 +196 val_196 196 val_196 196 val_196 +196 val_196 +196 val_196 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 +197 val_197 197 val_197 197 val_197 197 val_197 @@ -861,6 +1627,22 @@ POSTHOOK: Input: default@test@p=2 197 val_197 197 val_197 197 val_197 +197 val_197 +197 val_197 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 +199 val_199 199 val_199 199 val_199 199 val_199 @@ -887,10 +1669,22 @@ POSTHOOK: Input: default@test@p=2 200 val_200 200 val_200 200 val_200 +200 val_200 +200 val_200 +200 val_200 +200 val_200 +201 val_201 +201 val_201 +201 val_201 +201 val_201 201 val_201 201 val_201 202 val_202 202 val_202 +202 val_202 +202 val_202 +202 val_202 +202 val_202 203 val_203 203 val_203 203 val_203 @@ -907,6 +1701,18 @@ POSTHOOK: Input: default@test@p=2 205 val_205 205 val_205 205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +205 val_205 +207 val_207 +207 val_207 207 val_207 207 val_207 207 val_207 @@ -915,6 +1721,14 @@ POSTHOOK: Input: default@test@p=2 207 val_207 207 val_207 207 val_207 +207 val_207 +207 val_207 +207 val_207 +207 val_207 +208 val_208 +208 val_208 +208 val_208 +208 val_208 208 val_208 208 val_208 208 val_208 @@ -933,6 +1747,22 @@ POSTHOOK: Input: default@test@p=2 208 val_208 208 val_208 208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +208 val_208 +209 val_209 +209 val_209 +209 val_209 +209 val_209 209 val_209 209 val_209 209 val_209 @@ -941,6 +1771,14 @@ POSTHOOK: Input: default@test@p=2 209 val_209 209 val_209 209 val_209 +209 val_209 +209 val_209 +213 val_213 +213 val_213 +213 val_213 +213 val_213 +213 val_213 +213 val_213 213 val_213 213 val_213 213 val_213 @@ -949,8 +1787,24 @@ POSTHOOK: Input: default@test@p=2 213 val_213 213 val_213 213 val_213 +213 val_213 +213 val_213 +213 val_213 +213 val_213 +213 val_213 +213 val_213 +214 val_214 +214 val_214 214 val_214 214 val_214 +214 val_214 +214 val_214 +216 val_216 +216 val_216 +216 val_216 +216 val_216 +216 val_216 +216 val_216 216 val_216 216 val_216 216 val_216 @@ -959,6 +1813,14 @@ POSTHOOK: Input: default@test@p=2 216 val_216 216 val_216 216 val_216 +216 val_216 +216 val_216 +216 val_216 +216 val_216 +216 val_216 +216 val_216 +217 val_217 +217 val_217 217 val_217 217 val_217 217 val_217 @@ -967,6 +1829,22 @@ POSTHOOK: Input: default@test@p=2 217 val_217 217 val_217 217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +217 val_217 +218 val_218 +218 val_218 +218 val_218 +218 val_218 +218 val_218 +218 val_218 218 val_218 218 val_218 219 val_219 @@ -977,6 +1855,22 @@ POSTHOOK: Input: default@test@p=2 219 val_219 219 val_219 219 val_219 +219 val_219 +219 val_219 +219 val_219 +219 val_219 +219 val_219 +219 val_219 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 +221 val_221 221 val_221 221 val_221 221 val_221 @@ -985,6 +1879,14 @@ POSTHOOK: Input: default@test@p=2 221 val_221 221 val_221 221 val_221 +221 val_221 +221 val_221 +222 val_222 +222 val_222 +222 val_222 +222 val_222 +222 val_222 +222 val_222 222 val_222 222 val_222 223 val_223 @@ -995,6 +1897,14 @@ POSTHOOK: Input: default@test@p=2 223 val_223 223 val_223 223 val_223 +223 val_223 +223 val_223 +223 val_223 +223 val_223 +224 val_224 +224 val_224 +224 val_224 +224 val_224 224 val_224 224 val_224 224 val_224 @@ -1003,10 +1913,26 @@ POSTHOOK: Input: default@test@p=2 224 val_224 224 val_224 224 val_224 +224 val_224 +224 val_224 +224 val_224 +224 val_224 +224 val_224 +224 val_224 +226 val_226 +226 val_226 226 val_226 226 val_226 228 val_228 228 val_228 +228 val_228 +228 val_228 +228 val_228 +228 val_228 +228 val_228 +228 val_228 +229 val_229 +229 val_229 229 val_229 229 val_229 229 val_229 @@ -1015,6 +1941,22 @@ POSTHOOK: Input: default@test@p=2 229 val_229 229 val_229 229 val_229 +229 val_229 +229 val_229 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 230 val_230 230 val_230 230 val_230 @@ -1065,6 +2007,26 @@ POSTHOOK: Input: default@test@p=2 230 val_230 230 val_230 230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +230 val_230 +233 val_233 +233 val_233 +233 val_233 +233 val_233 +233 val_233 +233 val_233 233 val_233 233 val_233 233 val_233 @@ -1075,6 +2037,10 @@ POSTHOOK: Input: default@test@p=2 233 val_233 235 val_235 235 val_235 +235 val_235 +235 val_235 +237 val_237 +237 val_237 237 val_237 237 val_237 237 val_237 @@ -1083,6 +2049,14 @@ POSTHOOK: Input: default@test@p=2 237 val_237 237 val_237 237 val_237 +237 val_237 +237 val_237 +237 val_237 +237 val_237 +237 val_237 +237 val_237 +238 val_238 +238 val_238 238 val_238 238 val_238 238 val_238 @@ -1091,6 +2065,14 @@ POSTHOOK: Input: default@test@p=2 238 val_238 238 val_238 238 val_238 +238 val_238 +238 val_238 +239 val_239 +239 val_239 +239 val_239 +239 val_239 +239 val_239 +239 val_239 239 val_239 239 val_239 239 val_239 @@ -1099,6 +2081,18 @@ POSTHOOK: Input: default@test@p=2 239 val_239 239 val_239 239 val_239 +239 val_239 +239 val_239 +239 val_239 +239 val_239 +241 val_241 +241 val_241 +241 val_241 +241 val_241 +241 val_241 +241 val_241 +241 val_241 +241 val_241 241 val_241 241 val_241 242 val_242 @@ -1109,14 +2103,24 @@ POSTHOOK: Input: default@test@p=2 242 val_242 242 val_242 242 val_242 +242 val_242 +242 val_242 244 val_244 244 val_244 247 val_247 247 val_247 +247 val_247 +247 val_247 +247 val_247 +247 val_247 248 val_248 248 val_248 249 val_249 249 val_249 +249 val_249 +249 val_249 +252 val_252 +252 val_252 252 val_252 252 val_252 255 val_255 @@ -1135,16 +2139,44 @@ POSTHOOK: Input: default@test@p=2 256 val_256 256 val_256 256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 +256 val_256 257 val_257 257 val_257 +257 val_257 +257 val_257 +258 val_258 +258 val_258 258 val_258 258 val_258 +258 val_258 +258 val_258 +260 val_260 +260 val_260 260 val_260 260 val_260 +260 val_260 +260 val_260 +260 val_260 +260 val_260 +262 val_262 +262 val_262 +262 val_262 +262 val_262 262 val_262 262 val_262 263 val_263 263 val_263 +263 val_263 +263 val_263 265 val_265 265 val_265 265 val_265 @@ -1155,6 +2187,12 @@ POSTHOOK: Input: default@test@p=2 265 val_265 266 val_266 266 val_266 +266 val_266 +266 val_266 +266 val_266 +266 val_266 +266 val_266 +266 val_266 272 val_272 272 val_272 272 val_272 @@ -1163,6 +2201,22 @@ POSTHOOK: Input: default@test@p=2 272 val_272 272 val_272 272 val_272 +272 val_272 +272 val_272 +272 val_272 +272 val_272 +272 val_272 +272 val_272 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 +273 val_273 273 val_273 273 val_273 273 val_273 @@ -1183,8 +2237,24 @@ POSTHOOK: Input: default@test@p=2 273 val_273 274 val_274 274 val_274 +274 val_274 +274 val_274 +274 val_274 +274 val_274 +274 val_274 +274 val_274 +275 val_275 +275 val_275 275 val_275 275 val_275 +275 val_275 +275 val_275 +275 val_275 +275 val_275 +275 val_275 +275 val_275 +277 val_277 +277 val_277 277 val_277 277 val_277 277 val_277 @@ -1217,6 +2287,22 @@ POSTHOOK: Input: default@test@p=2 277 val_277 277 val_277 277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +277 val_277 +278 val_278 +278 val_278 278 val_278 278 val_278 278 val_278 @@ -1225,6 +2311,14 @@ POSTHOOK: Input: default@test@p=2 278 val_278 278 val_278 278 val_278 +278 val_278 +278 val_278 +278 val_278 +278 val_278 +280 val_280 +280 val_280 +280 val_280 +280 val_280 280 val_280 280 val_280 280 val_280 @@ -1233,6 +2327,14 @@ POSTHOOK: Input: default@test@p=2 280 val_280 280 val_280 280 val_280 +280 val_280 +280 val_280 +280 val_280 +280 val_280 +281 val_281 +281 val_281 +281 val_281 +281 val_281 281 val_281 281 val_281 281 val_281 @@ -1241,6 +2343,14 @@ POSTHOOK: Input: default@test@p=2 281 val_281 281 val_281 281 val_281 +281 val_281 +281 val_281 +281 val_281 +281 val_281 +282 val_282 +282 val_282 +282 val_282 +282 val_282 282 val_282 282 val_282 282 val_282 @@ -1249,16 +2359,40 @@ POSTHOOK: Input: default@test@p=2 282 val_282 282 val_282 282 val_282 +282 val_282 +282 val_282 +283 val_283 +283 val_283 283 val_283 283 val_283 284 val_284 284 val_284 +284 val_284 +284 val_284 +285 val_285 +285 val_285 285 val_285 285 val_285 +285 val_285 +285 val_285 +286 val_286 +286 val_286 +286 val_286 +286 val_286 +286 val_286 +286 val_286 286 val_286 286 val_286 287 val_287 287 val_287 +287 val_287 +287 val_287 +287 val_287 +287 val_287 +288 val_288 +288 val_288 +288 val_288 +288 val_288 288 val_288 288 val_288 288 val_288 @@ -1267,14 +2401,46 @@ POSTHOOK: Input: default@test@p=2 288 val_288 288 val_288 288 val_288 +288 val_288 +288 val_288 +288 val_288 +288 val_288 +288 val_288 +288 val_288 +289 val_289 +289 val_289 289 val_289 289 val_289 +289 val_289 +289 val_289 +289 val_289 +289 val_289 +289 val_289 +289 val_289 +291 val_291 +291 val_291 +291 val_291 +291 val_291 +291 val_291 +291 val_291 291 val_291 291 val_291 292 val_292 292 val_292 +292 val_292 +292 val_292 +296 val_296 +296 val_296 296 val_296 296 val_296 +296 val_296 +296 val_296 +296 val_296 +296 val_296 +298 val_298 +298 val_298 +298 val_298 +298 val_298 298 val_298 298 val_298 298 val_298 @@ -1293,12 +2459,44 @@ POSTHOOK: Input: default@test@p=2 298 val_298 298 val_298 298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +298 val_298 +302 val_302 +302 val_302 +302 val_302 +302 val_302 +302 val_302 +302 val_302 302 val_302 302 val_302 305 val_305 305 val_305 +305 val_305 +305 val_305 +305 val_305 +305 val_305 +306 val_306 +306 val_306 +306 val_306 +306 val_306 306 val_306 306 val_306 +306 val_306 +306 val_306 +307 val_307 +307 val_307 +307 val_307 +307 val_307 +307 val_307 +307 val_307 307 val_307 307 val_307 307 val_307 @@ -1309,6 +2507,14 @@ POSTHOOK: Input: default@test@p=2 307 val_307 308 val_308 308 val_308 +308 val_308 +308 val_308 +308 val_308 +308 val_308 +309 val_309 +309 val_309 +309 val_309 +309 val_309 309 val_309 309 val_309 309 val_309 @@ -1319,6 +2525,24 @@ POSTHOOK: Input: default@test@p=2 309 val_309 310 val_310 310 val_310 +310 val_310 +310 val_310 +310 val_310 +310 val_310 +310 val_310 +310 val_310 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 +311 val_311 311 val_311 311 val_311 311 val_311 @@ -1357,6 +2581,28 @@ POSTHOOK: Input: default@test@p=2 316 val_316 316 val_316 316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +316 val_316 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 +317 val_317 317 val_317 317 val_317 317 val_317 @@ -1383,6 +2629,22 @@ POSTHOOK: Input: default@test@p=2 318 val_318 318 val_318 318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +318 val_318 +321 val_321 +321 val_321 321 val_321 321 val_321 321 val_321 @@ -1391,6 +2653,14 @@ POSTHOOK: Input: default@test@p=2 321 val_321 321 val_321 321 val_321 +321 val_321 +321 val_321 +322 val_322 +322 val_322 +322 val_322 +322 val_322 +322 val_322 +322 val_322 322 val_322 322 val_322 322 val_322 @@ -1401,6 +2671,14 @@ POSTHOOK: Input: default@test@p=2 322 val_322 323 val_323 323 val_323 +323 val_323 +323 val_323 +323 val_323 +323 val_323 +323 val_323 +323 val_323 +323 val_323 +323 val_323 325 val_325 325 val_325 325 val_325 @@ -1409,6 +2687,30 @@ POSTHOOK: Input: default@test@p=2 325 val_325 325 val_325 325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +325 val_325 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 +327 val_327 327 val_327 327 val_327 327 val_327 @@ -1435,8 +2737,30 @@ POSTHOOK: Input: default@test@p=2 331 val_331 331 val_331 331 val_331 +331 val_331 +331 val_331 +331 val_331 +331 val_331 332 val_332 332 val_332 +332 val_332 +332 val_332 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 +333 val_333 333 val_333 333 val_333 333 val_333 @@ -1449,12 +2773,30 @@ POSTHOOK: Input: default@test@p=2 335 val_335 336 val_336 336 val_336 +336 val_336 +336 val_336 338 val_338 338 val_338 +338 val_338 +338 val_338 +338 val_338 +338 val_338 +338 val_338 +338 val_338 +339 val_339 +339 val_339 339 val_339 339 val_339 +339 val_339 +339 val_339 +341 val_341 +341 val_341 341 val_341 341 val_341 +341 val_341 +341 val_341 +342 val_342 +342 val_342 342 val_342 342 val_342 342 val_342 @@ -1463,6 +2805,22 @@ POSTHOOK: Input: default@test@p=2 342 val_342 342 val_342 342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +342 val_342 +344 val_344 +344 val_344 +344 val_344 +344 val_344 +344 val_344 +344 val_344 344 val_344 344 val_344 344 val_344 @@ -1471,8 +2829,24 @@ POSTHOOK: Input: default@test@p=2 344 val_344 344 val_344 344 val_344 +344 val_344 +344 val_344 +344 val_344 +344 val_344 +344 val_344 +344 val_344 +345 val_345 +345 val_345 345 val_345 345 val_345 +345 val_345 +345 val_345 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 348 val_348 348 val_348 348 val_348 @@ -1523,8 +2897,48 @@ POSTHOOK: Input: default@test@p=2 348 val_348 348 val_348 348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +348 val_348 +351 val_351 +351 val_351 351 val_351 351 val_351 +351 val_351 +351 val_351 +351 val_351 +351 val_351 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 +353 val_353 353 val_353 353 val_353 353 val_353 @@ -1535,16 +2949,48 @@ POSTHOOK: Input: default@test@p=2 353 val_353 356 val_356 356 val_356 +356 val_356 +356 val_356 +356 val_356 +356 val_356 +360 val_360 +360 val_360 +360 val_360 +360 val_360 360 val_360 360 val_360 362 val_362 362 val_362 +362 val_362 +362 val_362 +362 val_362 +362 val_362 +362 val_362 +362 val_362 +364 val_364 +364 val_364 364 val_364 364 val_364 +364 val_364 +364 val_364 +365 val_365 +365 val_365 365 val_365 365 val_365 +365 val_365 +365 val_365 +366 val_366 +366 val_366 366 val_366 366 val_366 +366 val_366 +366 val_366 +366 val_366 +366 val_366 +367 val_367 +367 val_367 +367 val_367 +367 val_367 367 val_367 367 val_367 367 val_367 @@ -1553,6 +2999,14 @@ POSTHOOK: Input: default@test@p=2 367 val_367 367 val_367 367 val_367 +367 val_367 +367 val_367 +368 val_368 +368 val_368 +368 val_368 +368 val_368 +368 val_368 +368 val_368 368 val_368 368 val_368 369 val_369 @@ -1573,16 +3027,56 @@ POSTHOOK: Input: default@test@p=2 369 val_369 369 val_369 369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +369 val_369 +373 val_373 +373 val_373 +373 val_373 +373 val_373 373 val_373 373 val_373 +373 val_373 +373 val_373 +373 val_373 +373 val_373 +374 val_374 +374 val_374 +374 val_374 +374 val_374 374 val_374 374 val_374 375 val_375 375 val_375 +375 val_375 +375 val_375 +375 val_375 +375 val_375 +377 val_377 +377 val_377 +377 val_377 +377 val_377 377 val_377 377 val_377 378 val_378 378 val_378 +378 val_378 +378 val_378 +378 val_378 +378 val_378 +379 val_379 +379 val_379 +379 val_379 +379 val_379 379 val_379 379 val_379 382 val_382 @@ -1593,6 +3087,14 @@ POSTHOOK: Input: default@test@p=2 382 val_382 382 val_382 382 val_382 +382 val_382 +382 val_382 +382 val_382 +382 val_382 +382 val_382 +382 val_382 +384 val_384 +384 val_384 384 val_384 384 val_384 384 val_384 @@ -1611,14 +3113,40 @@ POSTHOOK: Input: default@test@p=2 384 val_384 384 val_384 384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +384 val_384 +386 val_386 +386 val_386 +386 val_386 +386 val_386 386 val_386 386 val_386 +386 val_386 +386 val_386 +389 val_389 +389 val_389 389 val_389 389 val_389 392 val_392 392 val_392 393 val_393 393 val_393 +393 val_393 +393 val_393 +393 val_393 +393 val_393 +393 val_393 +393 val_393 394 val_394 394 val_394 395 val_395 @@ -1629,6 +3157,20 @@ POSTHOOK: Input: default@test@p=2 395 val_395 395 val_395 395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +395 val_395 +396 val_396 +396 val_396 +396 val_396 +396 val_396 396 val_396 396 val_396 396 val_396 @@ -1647,6 +3189,22 @@ POSTHOOK: Input: default@test@p=2 396 val_396 396 val_396 396 val_396 +396 val_396 +396 val_396 +396 val_396 +396 val_396 +396 val_396 +396 val_396 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 +397 val_397 397 val_397 397 val_397 397 val_397 @@ -1663,6 +3221,12 @@ POSTHOOK: Input: default@test@p=2 399 val_399 399 val_399 399 val_399 +399 val_399 +399 val_399 +399 val_399 +399 val_399 +399 val_399 +399 val_399 400 val_400 400 val_400 401 val_401 @@ -1715,8 +3279,26 @@ POSTHOOK: Input: default@test@p=2 401 val_401 401 val_401 401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 +401 val_401 402 val_402 402 val_402 +402 val_402 +402 val_402 +403 val_403 +403 val_403 403 val_403 403 val_403 403 val_403 @@ -1735,6 +3317,22 @@ POSTHOOK: Input: default@test@p=2 403 val_403 403 val_403 403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +403 val_403 +404 val_404 +404 val_404 +404 val_404 +404 val_404 404 val_404 404 val_404 404 val_404 @@ -1743,6 +3341,30 @@ POSTHOOK: Input: default@test@p=2 404 val_404 404 val_404 404 val_404 +404 val_404 +404 val_404 +404 val_404 +404 val_404 +404 val_404 +404 val_404 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 +406 val_406 406 val_406 406 val_406 406 val_406 @@ -1777,6 +3399,14 @@ POSTHOOK: Input: default@test@p=2 406 val_406 407 val_407 407 val_407 +407 val_407 +407 val_407 +407 val_407 +407 val_407 +409 val_409 +409 val_409 +409 val_409 +409 val_409 409 val_409 409 val_409 409 val_409 @@ -1795,8 +3425,24 @@ POSTHOOK: Input: default@test@p=2 409 val_409 409 val_409 409 val_409 +409 val_409 +409 val_409 +409 val_409 +409 val_409 +411 val_411 +411 val_411 +411 val_411 +411 val_411 411 val_411 411 val_411 +411 val_411 +411 val_411 +411 val_411 +411 val_411 +413 val_413 +413 val_413 +413 val_413 +413 val_413 413 val_413 413 val_413 413 val_413 @@ -1805,6 +3451,22 @@ POSTHOOK: Input: default@test@p=2 413 val_413 413 val_413 413 val_413 +413 val_413 +413 val_413 +413 val_413 +413 val_413 +413 val_413 +413 val_413 +413 val_413 +413 val_413 +414 val_414 +414 val_414 +414 val_414 +414 val_414 +414 val_414 +414 val_414 +414 val_414 +414 val_414 414 val_414 414 val_414 414 val_414 @@ -1813,6 +3475,14 @@ POSTHOOK: Input: default@test@p=2 414 val_414 414 val_414 414 val_414 +414 val_414 +414 val_414 +417 val_417 +417 val_417 +417 val_417 +417 val_417 +417 val_417 +417 val_417 417 val_417 417 val_417 417 val_417 @@ -1831,12 +3501,36 @@ POSTHOOK: Input: default@test@p=2 417 val_417 417 val_417 417 val_417 +417 val_417 +417 val_417 +417 val_417 +417 val_417 +417 val_417 +417 val_417 +418 val_418 +418 val_418 +418 val_418 +418 val_418 +418 val_418 +418 val_418 418 val_418 418 val_418 419 val_419 419 val_419 421 val_421 421 val_421 +421 val_421 +421 val_421 +421 val_421 +421 val_421 +424 val_424 +424 val_424 +424 val_424 +424 val_424 +424 val_424 +424 val_424 +424 val_424 +424 val_424 424 val_424 424 val_424 424 val_424 @@ -1847,6 +3541,14 @@ POSTHOOK: Input: default@test@p=2 424 val_424 427 val_427 427 val_427 +427 val_427 +427 val_427 +429 val_429 +429 val_429 +429 val_429 +429 val_429 +429 val_429 +429 val_429 429 val_429 429 val_429 429 val_429 @@ -1855,6 +3557,24 @@ POSTHOOK: Input: default@test@p=2 429 val_429 429 val_429 429 val_429 +429 val_429 +429 val_429 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 +430 val_430 430 val_430 430 val_430 430 val_430 @@ -1893,12 +3613,34 @@ POSTHOOK: Input: default@test@p=2 431 val_431 432 val_432 432 val_432 +432 val_432 +432 val_432 +432 val_432 +432 val_432 +435 val_435 +435 val_435 435 val_435 435 val_435 436 val_436 436 val_436 +436 val_436 +436 val_436 +436 val_436 +436 val_436 +437 val_437 +437 val_437 +437 val_437 +437 val_437 437 val_437 437 val_437 +437 val_437 +437 val_437 +437 val_437 +437 val_437 +438 val_438 +438 val_438 +438 val_438 +438 val_438 438 val_438 438 val_438 438 val_438 @@ -1917,6 +3659,22 @@ POSTHOOK: Input: default@test@p=2 438 val_438 438 val_438 438 val_438 +438 val_438 +438 val_438 +438 val_438 +438 val_438 +438 val_438 +438 val_438 +438 val_438 +438 val_438 +439 val_439 +439 val_439 +439 val_439 +439 val_439 +439 val_439 +439 val_439 +439 val_439 +439 val_439 439 val_439 439 val_439 439 val_439 @@ -1927,16 +3685,44 @@ POSTHOOK: Input: default@test@p=2 439 val_439 443 val_443 443 val_443 +443 val_443 +443 val_443 +443 val_443 +443 val_443 +443 val_443 +443 val_443 +443 val_443 +443 val_443 444 val_444 444 val_444 +444 val_444 +444 val_444 +446 val_446 +446 val_446 +446 val_446 +446 val_446 +446 val_446 +446 val_446 446 val_446 446 val_446 448 val_448 448 val_448 +448 val_448 +448 val_448 +448 val_448 +448 val_448 +448 val_448 +448 val_448 +449 val_449 +449 val_449 449 val_449 449 val_449 452 val_452 452 val_452 +452 val_452 +452 val_452 +453 val_453 +453 val_453 453 val_453 453 val_453 454 val_454 @@ -1957,10 +3743,46 @@ POSTHOOK: Input: default@test@p=2 454 val_454 454 val_454 454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +454 val_454 +455 val_455 +455 val_455 455 val_455 455 val_455 +455 val_455 +455 val_455 +457 val_457 +457 val_457 457 val_457 457 val_457 +457 val_457 +457 val_457 +457 val_457 +457 val_457 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 +458 val_458 458 val_458 458 val_458 458 val_458 @@ -1977,8 +3799,32 @@ POSTHOOK: Input: default@test@p=2 459 val_459 459 val_459 459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +459 val_459 +460 val_460 +460 val_460 +460 val_460 +460 val_460 460 val_460 460 val_460 +460 val_460 +460 val_460 +462 val_462 +462 val_462 +462 val_462 +462 val_462 +462 val_462 +462 val_462 462 val_462 462 val_462 462 val_462 @@ -1987,6 +3833,18 @@ POSTHOOK: Input: default@test@p=2 462 val_462 462 val_462 462 val_462 +462 val_462 +462 val_462 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 +463 val_463 463 val_463 463 val_463 463 val_463 @@ -2015,6 +3873,18 @@ POSTHOOK: Input: default@test@p=2 466 val_466 467 val_467 467 val_467 +467 val_467 +467 val_467 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 468 val_468 468 val_468 468 val_468 @@ -2047,6 +3917,22 @@ POSTHOOK: Input: default@test@p=2 468 val_468 468 val_468 468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +468 val_468 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 469 val_469 469 val_469 469 val_469 @@ -2097,14 +3983,54 @@ POSTHOOK: Input: default@test@p=2 469 val_469 469 val_469 469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +469 val_469 +470 val_470 +470 val_470 +470 val_470 +470 val_470 +470 val_470 +470 val_470 470 val_470 470 val_470 472 val_472 472 val_472 +472 val_472 +472 val_472 +475 val_475 +475 val_475 475 val_475 475 val_475 +475 val_475 +475 val_475 +477 val_477 +477 val_477 477 val_477 477 val_477 +477 val_477 +477 val_477 +478 val_478 +478 val_478 +478 val_478 +478 val_478 +478 val_478 +478 val_478 478 val_478 478 val_478 478 val_478 @@ -2113,6 +4039,14 @@ POSTHOOK: Input: default@test@p=2 478 val_478 478 val_478 478 val_478 +478 val_478 +478 val_478 +478 val_478 +478 val_478 +479 val_479 +479 val_479 +479 val_479 +479 val_479 479 val_479 479 val_479 480 val_480 @@ -2133,16 +4067,48 @@ POSTHOOK: Input: default@test@p=2 480 val_480 480 val_480 480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +480 val_480 +481 val_481 +481 val_481 +481 val_481 +481 val_481 +481 val_481 +481 val_481 481 val_481 481 val_481 482 val_482 482 val_482 483 val_483 483 val_483 +483 val_483 +483 val_483 +484 val_484 +484 val_484 +484 val_484 +484 val_484 484 val_484 484 val_484 485 val_485 485 val_485 +485 val_485 +485 val_485 +487 val_487 +487 val_487 487 val_487 487 val_487 489 val_489 @@ -2177,10 +4143,46 @@ POSTHOOK: Input: default@test@p=2 489 val_489 489 val_489 489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +489 val_489 +490 val_490 +490 val_490 490 val_490 490 val_490 +490 val_490 +490 val_490 +491 val_491 +491 val_491 491 val_491 491 val_491 +491 val_491 +491 val_491 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 +492 val_492 492 val_492 492 val_492 492 val_492 @@ -2193,10 +4195,22 @@ POSTHOOK: Input: default@test@p=2 493 val_493 494 val_494 494 val_494 +494 val_494 +494 val_494 +495 val_495 +495 val_495 495 val_495 495 val_495 +495 val_495 +495 val_495 +496 val_496 +496 val_496 496 val_496 496 val_496 +496 val_496 +496 val_496 +497 val_497 +497 val_497 497 val_497 497 val_497 498 val_498 diff --git ql/src/test/results/clientpositive/tez/tez_join_hash.q.out ql/src/test/results/clientpositive/tez/tez_join_hash.q.out index 09925c1..a958549 100644 --- ql/src/test/results/clientpositive/tez/tez_join_hash.q.out +++ ql/src/test/results/clientpositive/tez/tez_join_hash.q.out @@ -92,7 +92,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -279,7 +278,6 @@ STAGE PLANS: Statistics: Num rows: 2310 Data size: 24541 Basic stats: COMPLETE Column stats: NONE value expressions: _col2 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -331,315 +329,315 @@ POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 #### A masked pattern was here #### -0 468 -10 20 -100 144 +0 484 +10 24 +100 160 103 144 -104 144 -105 20 -11 20 -111 20 -113 144 -114 20 -116 20 -118 144 -119 468 -12 144 +104 284 +105 56 +11 64 +111 56 +113 280 +114 64 +116 56 +118 280 +119 776 +12 288 120 144 -125 144 -126 20 -128 468 -129 144 -131 20 -133 20 -134 144 -136 20 -137 144 -138 1088 -143 20 -145 20 +125 288 +126 56 +128 784 +129 284 +131 60 +133 60 +134 280 +136 60 +137 292 +138 1620 +143 56 +145 60 146 144 -149 144 -15 144 -150 20 -152 144 -153 20 -155 20 -156 20 -157 20 -158 20 -160 20 -162 20 -163 20 +149 284 +15 284 +150 64 +152 288 +153 60 +155 56 +156 60 +157 56 +158 60 +160 56 +162 60 +163 60 164 144 -165 144 -166 20 -167 468 +165 284 +166 60 +167 776 168 20 -169 1088 +169 1376 17 20 -170 20 -172 144 -174 144 -175 144 -176 144 -177 20 -178 20 -179 144 -18 144 -180 20 -181 20 -183 20 -186 20 -187 468 -189 20 -19 20 -190 20 -191 144 +170 40 +172 224 +174 224 +175 220 +176 220 +177 48 +178 56 +179 220 +18 220 +180 44 +181 56 +183 44 +186 44 +187 632 +189 44 +19 40 +190 48 +191 220 192 20 -193 468 -194 20 -195 144 -196 20 -197 144 -199 468 -2 20 -20 20 +193 636 +194 44 +195 220 +196 44 +197 224 +199 632 +2 48 +20 40 200 144 201 20 -202 20 -203 144 -205 144 -207 144 -208 468 -209 144 -213 144 -214 20 -216 144 -217 144 -218 20 -219 144 -221 144 -222 20 -223 144 -224 144 +202 48 +203 216 +205 232 +207 224 +208 644 +209 220 +213 228 +214 44 +216 220 +217 220 +218 44 +219 220 +221 220 +222 40 +223 216 +224 224 226 20 228 20 -229 144 +229 224 230 2100 -233 144 +233 476 235 20 -237 144 -238 144 -239 144 +237 224 +238 218 +239 220 24 144 -241 20 -242 144 -244 20 -247 20 -248 20 -249 20 -252 20 -255 144 -256 144 -257 20 +241 60 +242 288 +244 64 +247 60 +248 60 +249 60 +252 60 +255 292 +256 280 +257 56 258 20 -26 144 -260 20 -262 20 -263 20 -265 144 +26 220 +260 56 +262 40 +263 40 +265 228 266 20 27 20 -272 144 -273 468 -274 20 -275 20 -277 1088 -278 144 -28 20 -280 144 -281 144 -282 144 +272 228 +273 636 +274 40 +275 44 +277 1368 +278 228 +28 44 +280 224 +281 224 +282 220 283 20 -284 20 -285 20 -286 20 -287 20 -288 144 -289 20 -291 20 -292 20 +284 44 +285 44 +286 40 +287 44 +288 220 +289 44 +291 44 +292 44 296 20 -298 468 -30 20 -302 20 -305 20 -306 20 -307 144 -308 20 -309 144 -310 20 -311 468 -315 20 -316 468 -317 144 -318 468 -321 144 -322 144 -323 20 +298 636 +30 40 +302 40 +305 40 +306 40 +307 220 +308 44 +309 220 +310 56 +311 628 +315 44 +316 640 +317 240 +318 636 +321 216 +322 220 +323 44 325 144 -327 468 -33 20 -331 144 -332 20 +327 776 +33 72 +331 288 +332 60 333 144 -335 20 -336 20 -338 20 -339 20 -34 20 +335 56 +336 64 +338 60 +339 56 +34 56 341 20 -342 144 -344 144 -345 20 -348 2100 -35 468 -351 20 +342 220 +344 220 +345 40 +348 2548 +35 640 +351 44 353 144 -356 20 -360 20 -362 20 -364 20 -365 20 -366 20 -367 144 +356 56 +360 60 +362 60 +364 56 +365 60 +366 56 +367 284 368 20 369 468 -37 144 -373 20 -374 20 -375 20 -377 20 +37 352 +373 76 +374 76 +375 76 +377 76 378 20 -379 20 -382 144 -384 468 -386 20 +379 40 +382 224 +384 640 +386 44 389 20 -392 20 -393 20 -394 20 -395 144 -396 468 +392 48 +393 44 +394 44 +395 224 +396 640 397 144 399 144 -4 20 -400 20 -401 2100 -402 20 -403 468 -404 144 -406 1088 -407 20 +4 56 +400 56 +401 2948 +402 64 +403 772 +404 300 +406 1632 +407 56 409 468 -41 20 -411 20 -413 144 -414 144 -417 468 -418 20 -419 20 -42 144 +41 72 +411 76 +413 356 +414 352 +417 920 +418 76 +419 72 +42 348 421 20 424 144 -427 20 -429 144 -43 20 -430 468 -431 468 -432 20 -435 20 -436 20 -437 20 -438 468 +427 56 +429 288 +43 60 +430 776 +431 780 +432 64 +435 56 +436 60 +437 60 +438 780 439 144 -44 20 -443 20 -444 20 -446 20 -448 20 -449 20 -452 20 -453 20 -454 468 -455 20 -457 20 -458 144 -459 144 -460 20 -462 144 -463 144 -466 468 -467 20 -468 1088 -469 2100 +44 56 +443 60 +444 56 +446 60 +448 60 +449 60 +452 64 +453 56 +454 780 +455 56 +457 60 +458 284 +459 284 +460 60 +462 284 +463 292 +466 776 +467 64 +468 1640 +469 2944 47 20 -470 20 -472 20 +470 48 +472 40 475 20 477 20 -478 144 +478 236 479 20 -480 468 -481 20 -482 20 -483 20 -484 20 -485 20 +480 632 +481 44 +482 44 +483 40 +484 48 +485 40 487 20 -489 1088 -490 20 -491 20 +489 1380 +490 40 +491 44 492 144 -493 20 -494 20 -495 20 -496 20 -497 20 -498 468 +493 60 +494 60 +495 60 +496 60 +497 64 +498 772 5 468 -51 144 -53 20 -54 20 -57 20 -58 144 -64 20 -65 20 -66 20 -67 144 -69 20 -70 468 -72 144 -74 20 -76 144 -77 20 -78 20 -8 20 -80 20 -82 20 -83 144 -84 144 +51 348 +53 72 +54 72 +57 72 +58 356 +64 72 +65 76 +66 80 +67 360 +69 72 +70 924 +72 344 +74 72 +76 344 +77 72 +78 72 +8 76 +80 72 +82 76 +83 352 +84 352 85 20 86 20 -87 20 -9 20 -90 468 +87 40 +9 44 +90 624 92 20 -95 144 -96 20 -97 144 -98 144 +95 228 +96 44 +97 224 +98 228 PREHOOK: query: select key, count(*) from (select x.key as key, y.value as value from srcpart x join srcpart y on (x.key = y.key) union all diff --git ql/src/test/results/clientpositive/tez/tez_smb_main.q.out ql/src/test/results/clientpositive/tez/tez_smb_main.q.out index 61d710f..4f502ec 100644 --- ql/src/test/results/clientpositive/tez/tez_smb_main.q.out +++ ql/src/test/results/clientpositive/tez/tez_smb_main.q.out @@ -337,7 +337,7 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -480 +496 PREHOOK: query: explain select count (*) from tab a join tab_part b on a.key = b.key @@ -443,7 +443,7 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -480 +522 PREHOOK: query: explain select count (*) from tab a join tab_part b on a.key = b.key @@ -549,7 +549,7 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -480 +522 PREHOOK: query: explain select count(*) from tab a join tab_part b on a.key = b.key join src1 c on a.value = c.value PREHOOK: type: QUERY POSTHOOK: query: explain select count(*) from tab a join tab_part b on a.key = b.key join src1 c on a.value = c.value @@ -1021,7 +1021,7 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -480 +510 PREHOOK: query: explain select count(*) from tab a join tab_part b on a.key = b.key join src1 c on a.value = c.value PREHOOK: type: QUERY POSTHOOK: query: explain select count(*) from tab a join tab_part b on a.key = b.key join src1 c on a.value = c.value @@ -1459,4 +1459,4 @@ POSTHOOK: Input: default@tab@ds=2008-04-08 POSTHOOK: Input: default@tab_part POSTHOOK: Input: default@tab_part@ds=2008-04-08 #### A masked pattern was here #### -480 +652 diff --git ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_1.q.out ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_1.q.out index 39eb1a4..8e9eef4 100644 --- ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_1.q.out +++ ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_1.q.out @@ -458,7 +458,6 @@ STAGE PLANS: value expressions: _col0 (type: tinyint), _col1 (type: smallint), _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: string), _col7 (type: string), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: boolean), _col11 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Map Join Operator condition map: @@ -477,7 +476,6 @@ STAGE PLANS: Statistics: Num rows: 3379 Data size: 726540 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: tinyint), _col1 (type: smallint), _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: string), _col7 (type: string), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: tinyint), _col13 (type: smallint), _col14 (type: int), _col15 (type: bigint), _col16 (type: float), _col17 (type: double), _col18 (type: string), _col19 (type: string), _col20 (type: timestamp), _col21 (type: timestamp), _col22 (type: boolean), _col23 (type: boolean) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: tinyint), VALUE._col1 (type: smallint), KEY.reducesinkkey0 (type: int), VALUE._col2 (type: bigint), VALUE._col3 (type: float), VALUE._col4 (type: double), VALUE._col5 (type: string), VALUE._col6 (type: string), VALUE._col7 (type: timestamp), VALUE._col8 (type: timestamp), VALUE._col9 (type: boolean), VALUE._col10 (type: boolean), VALUE._col11 (type: tinyint), VALUE._col12 (type: smallint), VALUE._col13 (type: int), VALUE._col14 (type: bigint), VALUE._col15 (type: float), VALUE._col16 (type: double), VALUE._col17 (type: string), VALUE._col18 (type: string), VALUE._col19 (type: timestamp), VALUE._col20 (type: timestamp), VALUE._col21 (type: boolean), VALUE._col22 (type: boolean) @@ -588,7 +586,6 @@ STAGE PLANS: Statistics: Num rows: 1536 Data size: 330245 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Map Join Operator condition map: @@ -610,7 +607,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -717,7 +713,6 @@ STAGE PLANS: Statistics: Num rows: 1536 Data size: 330245 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Map Join Operator condition map: @@ -743,7 +738,6 @@ STAGE PLANS: Statistics: Num rows: 3379 Data size: 726540 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -757,7 +751,6 @@ STAGE PLANS: Statistics: Num rows: 1689 Data size: 363162 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: smallint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: smallint), KEY.reducesinkkey0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_2.q.out ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_2.q.out index d8bc128..15a6712 100644 --- ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_2.q.out +++ ql/src/test/results/clientpositive/tez/tez_vector_dynpart_hashjoin_2.q.out @@ -278,7 +278,6 @@ STAGE PLANS: Map-reduce partition columns: (UDFToInteger(_col0) + 0) (type: int) Statistics: Num rows: 250 Data size: 2656 Basic stats: COMPLETE Column stats: NONE Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Map Join Operator condition map: @@ -300,7 +299,6 @@ STAGE PLANS: Statistics: Num rows: 4505 Data size: 968719 Basic stats: COMPLETE Column stats: NONE value expressions: _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: string), _col7 (type: string), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: boolean), _col11 (type: boolean) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey1 (type: tinyint), KEY.reducesinkkey0 (type: smallint), KEY.reducesinkkey2 (type: int), VALUE._col0 (type: bigint), VALUE._col1 (type: float), VALUE._col2 (type: double), VALUE._col3 (type: string), VALUE._col4 (type: string), VALUE._col5 (type: timestamp), VALUE._col6 (type: timestamp), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean) @@ -469,7 +467,6 @@ STAGE PLANS: Map-reduce partition columns: (UDFToInteger(_col0) + 0) (type: int) Statistics: Num rows: 250 Data size: 2656 Basic stats: COMPLETE Column stats: NONE Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Map Join Operator condition map: @@ -491,7 +488,6 @@ STAGE PLANS: Statistics: Num rows: 4505 Data size: 968719 Basic stats: COMPLETE Column stats: NONE value expressions: _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: string), _col7 (type: string), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: boolean), _col11 (type: boolean) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey1 (type: tinyint), KEY.reducesinkkey0 (type: smallint), KEY.reducesinkkey2 (type: int), VALUE._col0 (type: bigint), VALUE._col1 (type: float), VALUE._col2 (type: double), VALUE._col3 (type: string), VALUE._col4 (type: string), VALUE._col5 (type: timestamp), VALUE._col6 (type: timestamp), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean) diff --git ql/src/test/results/clientpositive/tez/unionDistinct_1.q.out ql/src/test/results/clientpositive/tez/unionDistinct_1.q.out index 22b6fad..7451969 100644 --- ql/src/test/results/clientpositive/tez/unionDistinct_1.q.out +++ ql/src/test/results/clientpositive/tez/unionDistinct_1.q.out @@ -6979,323 +6979,640 @@ POSTHOOK: Input: default@dst_union22@ds=2 0 val_0 0 val_0 2 10 val_10 10 val_10 2 100 val_100 100 val_100 2 +100 val_100 454 val_454 2 +100 val_100 492 val_492 2 103 val_103 103 val_103 2 +103 val_103 386 val_386 2 104 val_104 104 val_104 2 +104 val_104 235 val_235 2 +104 val_104 454 val_454 2 105 val_105 105 val_105 2 +105 val_105 235 val_235 2 11 val_11 11 val_11 2 111 val_111 111 val_111 2 113 val_113 113 val_113 2 +113 val_113 495 val_495 2 114 val_114 114 val_114 2 +114 val_114 257 val_257 2 116 val_116 116 val_116 2 118 val_118 118 val_118 2 +118 val_118 134 val_134 2 +118 val_118 179 val_179 2 119 val_119 119 val_119 2 +119 val_119 172 val_172 2 +119 val_119 233 val_233 2 +119 val_119 454 val_454 2 12 val_12 12 val_12 2 120 val_120 120 val_120 2 +120 val_120 454 val_454 2 125 val_125 125 val_125 2 +125 val_125 454 val_454 2 126 val_126 126 val_126 2 +126 val_126 97 val_97 2 128 val_128 128 val_128 2 +128 val_128 155 val_155 2 129 val_129 129 val_129 2 +129 val_129 155 val_155 2 +129 val_129 172 val_172 2 131 val_131 131 val_131 2 133 val_133 133 val_133 2 +133 val_133 233 val_233 2 134 val_134 134 val_134 2 136 val_136 136 val_136 2 +136 val_136 333 val_333 2 137 val_137 137 val_137 2 +137 val_137 386 val_386 2 +138 val_138 134 val_134 2 138 val_138 138 val_138 2 +138 val_138 353 val_353 2 +138 val_138 386 val_386 2 +138 val_138 492 val_492 2 143 val_143 143 val_143 2 +143 val_143 233 val_233 2 145 val_145 145 val_145 2 146 val_146 146 val_146 2 +146 val_146 492 val_492 2 149 val_149 149 val_149 2 +149 val_149 155 val_155 2 +149 val_149 492 val_492 2 15 val_15 15 val_15 2 150 val_150 150 val_150 2 152 val_152 152 val_152 2 +152 val_152 97 val_97 2 153 val_153 153 val_153 2 +153 val_153 495 val_495 2 155 val_155 155 val_155 2 156 val_156 156 val_156 2 157 val_157 157 val_157 2 +157 val_157 170 val_170 2 158 val_158 158 val_158 2 +158 val_158 172 val_172 2 160 val_160 160 val_160 2 +160 val_160 233 val_233 2 162 val_162 162 val_162 2 +162 val_162 495 val_495 2 163 val_163 163 val_163 2 +163 val_163 454 val_454 2 164 val_164 164 val_164 2 +164 val_164 257 val_257 2 +164 val_164 454 val_454 2 165 val_165 165 val_165 2 166 val_166 166 val_166 2 +166 val_166 495 val_495 2 167 val_167 167 val_167 2 +167 val_167 492 val_492 2 +167 val_167 495 val_495 2 168 val_168 168 val_168 2 +168 val_168 392 val_392 2 169 val_169 169 val_169 2 +169 val_169 454 val_454 2 +169 val_169 97 val_97 2 17 val_17 17 val_17 2 170 val_170 170 val_170 2 172 val_172 172 val_172 2 174 val_174 174 val_174 2 +174 val_174 495 val_495 2 175 val_175 175 val_175 2 +175 val_175 233 val_233 2 +175 val_175 235 val_235 2 176 val_176 176 val_176 2 +176 val_176 386 val_386 2 177 val_177 177 val_177 2 +177 val_177 392 val_392 2 178 val_178 178 val_178 2 +178 val_178 323 val_323 2 179 val_179 179 val_179 2 18 val_18 18 val_18 2 180 val_180 180 val_180 2 +180 val_180 333 val_333 2 181 val_181 181 val_181 2 +181 val_181 333 val_333 2 183 val_183 183 val_183 2 +183 val_183 492 val_492 2 186 val_186 186 val_186 2 +186 val_186 492 val_492 2 187 val_187 187 val_187 2 +187 val_187 386 val_386 2 +187 val_187 454 val_454 2 189 val_189 189 val_189 2 +189 val_189 233 val_233 2 19 val_19 19 val_19 2 190 val_190 190 val_190 2 +190 val_190 257 val_257 2 191 val_191 191 val_191 2 +191 val_191 454 val_454 2 192 val_192 192 val_192 2 +192 val_192 386 val_386 2 193 val_193 193 val_193 2 +193 val_193 235 val_235 2 +193 val_193 495 val_495 2 194 val_194 194 val_194 2 +194 val_194 97 val_97 2 195 val_195 195 val_195 2 +195 val_195 233 val_233 2 +195 val_195 495 val_495 2 196 val_196 196 val_196 2 +197 val_197 172 val_172 2 197 val_197 197 val_197 2 199 val_199 199 val_199 2 +199 val_199 454 val_454 2 +199 val_199 495 val_495 2 2 val_2 2 val_2 2 20 val_20 20 val_20 2 200 val_200 200 val_200 2 +200 val_200 454 val_454 2 +200 val_200 97 val_97 2 201 val_201 201 val_201 2 +201 val_201 257 val_257 2 202 val_202 202 val_202 2 +203 val_203 155 val_155 2 203 val_203 203 val_203 2 +203 val_203 257 val_257 2 +205 val_205 155 val_155 2 205 val_205 205 val_205 2 207 val_207 207 val_207 2 +207 val_207 333 val_333 2 +207 val_207 495 val_495 2 208 val_208 208 val_208 2 +208 val_208 411 val_411 2 +208 val_208 495 val_495 2 209 val_209 209 val_209 2 +209 val_209 495 val_495 2 213 val_213 213 val_213 2 +213 val_213 386 val_386 2 +214 val_214 172 val_172 2 214 val_214 214 val_214 2 216 val_216 216 val_216 2 +216 val_216 386 val_386 2 217 val_217 217 val_217 2 +217 val_217 257 val_257 2 +217 val_217 353 val_353 2 218 val_218 218 val_218 2 +218 val_218 492 val_492 2 219 val_219 219 val_219 2 +219 val_219 495 val_495 2 +221 val_221 170 val_170 2 221 val_221 221 val_221 2 +221 val_221 386 val_386 2 222 val_222 222 val_222 2 +222 val_222 97 val_97 2 223 val_223 223 val_223 2 +223 val_223 233 val_233 2 +224 val_224 134 val_134 2 224 val_224 224 val_224 2 226 val_226 226 val_226 2 +226 val_226 392 val_392 2 228 val_228 228 val_228 2 +228 val_228 492 val_492 2 229 val_229 229 val_229 2 +229 val_229 392 val_392 2 230 val_230 230 val_230 2 +230 val_230 323 val_323 2 +230 val_230 333 val_333 2 +230 val_230 411 val_411 2 233 val_233 233 val_233 2 235 val_235 235 val_235 2 237 val_237 237 val_237 2 +237 val_237 454 val_454 2 +237 val_237 495 val_495 2 +238 val_238 134 val_134 2 238 val_238 238 val_238 2 239 val_239 239 val_239 2 +239 val_239 386 val_386 2 24 val_24 NULL NULL 2 24 val_24 24 val_24 2 +24 val_24 411 val_411 2 +24 val_24 454 val_454 2 241 val_241 241 val_241 2 +241 val_241 386 val_386 2 +242 val_242 134 val_134 2 242 val_242 242 val_242 2 +242 val_242 392 val_392 2 244 val_244 244 val_244 2 247 val_247 247 val_247 2 +247 val_247 495 val_495 2 248 val_248 248 val_248 2 +248 val_248 454 val_454 2 249 val_249 249 val_249 2 +249 val_249 333 val_333 2 252 val_252 252 val_252 2 +252 val_252 495 val_495 2 255 val_255 255 val_255 2 +255 val_255 454 val_454 2 256 val_256 256 val_256 2 +256 val_256 407 val_407 2 257 val_257 257 val_257 2 +258 val_258 257 val_257 2 258 val_258 258 val_258 2 26 val_26 NULL NULL 2 26 val_26 26 val_26 2 260 val_260 260 val_260 2 +260 val_260 333 val_333 2 +262 val_262 257 val_257 2 262 val_262 262 val_262 2 263 val_263 263 val_263 2 265 val_265 265 val_265 2 +265 val_265 333 val_333 2 266 val_266 266 val_266 2 +266 val_266 495 val_495 2 27 val_27 NULL NULL 2 27 val_27 27 val_27 2 272 val_272 272 val_272 2 +272 val_272 353 val_353 2 +272 val_272 392 val_392 2 +273 val_273 134 val_134 2 273 val_273 273 val_273 2 +273 val_273 492 val_492 2 274 val_274 274 val_274 2 275 val_275 275 val_275 2 +275 val_275 386 val_386 2 +277 val_277 134 val_134 2 277 val_277 277 val_277 2 +277 val_277 323 val_323 2 +277 val_277 495 val_495 2 278 val_278 278 val_278 2 +278 val_278 386 val_386 2 28 val_28 NULL NULL 2 28 val_28 28 val_28 2 +28 val_28 97 val_97 2 280 val_280 280 val_280 2 281 val_281 281 val_281 2 +281 val_281 492 val_492 2 +281 val_281 495 val_495 2 +282 val_282 134 val_134 2 282 val_282 282 val_282 2 283 val_283 283 val_283 2 284 val_284 284 val_284 2 +284 val_284 333 val_333 2 285 val_285 285 val_285 2 +285 val_285 492 val_492 2 286 val_286 286 val_286 2 +286 val_286 386 val_386 2 287 val_287 287 val_287 2 +287 val_287 495 val_495 2 288 val_288 288 val_288 2 289 val_289 289 val_289 2 +289 val_289 386 val_386 2 291 val_291 291 val_291 2 +291 val_291 454 val_454 2 292 val_292 292 val_292 2 +292 val_292 495 val_495 2 296 val_296 296 val_296 2 298 val_298 298 val_298 2 +298 val_298 454 val_454 2 +298 val_298 492 val_492 2 30 val_30 NULL NULL 2 30 val_30 30 val_30 2 +302 val_302 155 val_155 2 302 val_302 302 val_302 2 305 val_305 305 val_305 2 +305 val_305 454 val_454 2 +306 val_306 134 val_134 2 306 val_306 306 val_306 2 +307 val_307 134 val_134 2 307 val_307 307 val_307 2 +307 val_307 97 val_97 2 308 val_308 308 val_308 2 +309 val_309 134 val_134 2 309 val_309 309 val_309 2 +309 val_309 495 val_495 2 310 val_310 310 val_310 2 +310 val_310 323 val_323 2 +311 val_311 155 val_155 2 311 val_311 311 val_311 2 +311 val_311 386 val_386 2 315 val_315 315 val_315 2 +315 val_315 97 val_97 2 +316 val_316 155 val_155 2 316 val_316 316 val_316 2 317 val_317 317 val_317 2 +317 val_317 323 val_323 2 +317 val_317 392 val_392 2 318 val_318 318 val_318 2 +318 val_318 386 val_386 2 +321 val_321 233 val_233 2 +321 val_321 235 val_235 2 321 val_321 321 val_321 2 +322 val_322 172 val_172 2 +322 val_322 233 val_233 2 322 val_322 322 val_322 2 323 val_323 323 val_323 2 +325 val_325 323 val_323 2 325 val_325 325 val_325 2 +325 val_325 495 val_495 2 +327 val_327 134 val_134 2 327 val_327 327 val_327 2 +327 val_327 495 val_495 2 33 val_33 NULL NULL 2 33 val_33 33 val_33 2 331 val_331 331 val_331 2 +331 val_331 392 val_392 2 332 val_332 332 val_332 2 +332 val_332 386 val_386 2 333 val_333 333 val_333 2 +335 val_335 235 val_235 2 335 val_335 335 val_335 2 336 val_336 336 val_336 2 +336 val_336 392 val_392 2 338 val_338 338 val_338 2 +338 val_338 495 val_495 2 +339 val_339 155 val_155 2 339 val_339 339 val_339 2 34 val_34 NULL NULL 2 34 val_34 34 val_34 2 +34 val_34 392 val_392 2 341 val_341 341 val_341 2 +341 val_341 492 val_492 2 342 val_342 342 val_342 2 +342 val_342 495 val_495 2 344 val_344 344 val_344 2 +344 val_344 492 val_492 2 +345 val_345 155 val_155 2 345 val_345 345 val_345 2 +348 val_348 257 val_257 2 348 val_348 348 val_348 2 +348 val_348 353 val_353 2 +348 val_348 411 val_411 2 +348 val_348 492 val_492 2 +348 val_348 97 val_97 2 35 val_35 NULL NULL 2 35 val_35 35 val_35 2 +35 val_35 392 val_392 2 351 val_351 351 val_351 2 +351 val_351 454 val_454 2 353 val_353 353 val_353 2 356 val_356 356 val_356 2 360 val_360 360 val_360 2 +360 val_360 454 val_454 2 362 val_362 362 val_362 2 +362 val_362 492 val_492 2 364 val_364 364 val_364 2 365 val_365 365 val_365 2 +365 val_365 495 val_495 2 +366 val_366 235 val_235 2 366 val_366 366 val_366 2 367 val_367 367 val_367 2 +367 val_367 495 val_495 2 368 val_368 368 val_368 2 +369 val_369 134 val_134 2 369 val_369 369 val_369 2 37 val_37 NULL NULL 2 37 val_37 37 val_37 2 +37 val_37 495 val_495 2 +37 val_37 97 val_97 2 +373 val_373 353 val_353 2 373 val_373 373 val_373 2 374 val_374 374 val_374 2 375 val_375 375 val_375 2 +375 val_375 454 val_454 2 377 val_377 377 val_377 2 +377 val_377 495 val_495 2 +378 val_378 170 val_170 2 378 val_378 378 val_378 2 +379 val_379 134 val_134 2 379 val_379 379 val_379 2 382 val_382 382 val_382 2 +382 val_382 454 val_454 2 +384 val_384 134 val_134 2 +384 val_384 333 val_333 2 384 val_384 384 val_384 2 +384 val_384 407 val_407 2 386 val_386 386 val_386 2 +389 val_389 134 val_134 2 389 val_389 389 val_389 2 392 val_392 392 val_392 2 +393 val_393 172 val_172 2 393 val_393 393 val_393 2 394 val_394 394 val_394 2 +394 val_394 495 val_495 2 +395 val_395 134 val_134 2 +395 val_395 392 val_392 2 395 val_395 395 val_395 2 +396 val_396 257 val_257 2 +396 val_396 392 val_392 2 396 val_396 396 val_396 2 +396 val_396 495 val_495 2 397 val_397 397 val_397 2 +397 val_397 454 val_454 2 +397 val_397 495 val_495 2 399 val_399 399 val_399 2 +399 val_399 495 val_495 2 4 val_4 4 val_4 2 400 val_400 400 val_400 2 +400 val_400 97 val_97 2 +401 val_401 257 val_257 2 +401 val_401 392 val_392 2 401 val_401 401 val_401 2 +401 val_401 454 val_454 2 +402 val_402 392 val_392 2 402 val_402 402 val_402 2 +403 val_403 235 val_235 2 403 val_403 403 val_403 2 +403 val_403 495 val_495 2 +403 val_403 97 val_97 2 +404 val_404 333 val_333 2 404 val_404 404 val_404 2 +406 val_406 233 val_233 2 +406 val_406 257 val_257 2 406 val_406 406 val_406 2 407 val_407 407 val_407 2 +409 val_409 257 val_257 2 409 val_409 409 val_409 2 41 val_41 NULL NULL 2 +41 val_41 233 val_233 2 41 val_41 41 val_41 2 411 val_411 411 val_411 2 413 val_413 413 val_413 2 +413 val_413 495 val_495 2 414 val_414 414 val_414 2 +414 val_414 454 val_454 2 +414 val_414 97 val_97 2 417 val_417 417 val_417 2 +417 val_417 454 val_454 2 +417 val_417 495 val_495 2 418 val_418 418 val_418 2 +418 val_418 454 val_454 2 +419 val_419 134 val_134 2 419 val_419 419 val_419 2 42 val_42 NULL NULL 2 +42 val_42 172 val_172 2 +42 val_42 233 val_233 2 42 val_42 42 val_42 2 421 val_421 421 val_421 2 +421 val_421 454 val_454 2 +424 val_424 257 val_257 2 424 val_424 424 val_424 2 +424 val_424 454 val_454 2 427 val_427 427 val_427 2 429 val_429 429 val_429 2 +429 val_429 454 val_454 2 43 val_43 NULL NULL 2 43 val_43 43 val_43 2 +430 val_430 233 val_233 2 +430 val_430 386 val_386 2 430 val_430 430 val_430 2 +430 val_430 495 val_495 2 +431 val_431 257 val_257 2 +431 val_431 411 val_411 2 431 val_431 431 val_431 2 +431 val_431 454 val_454 2 432 val_432 432 val_432 2 +435 val_435 134 val_134 2 435 val_435 435 val_435 2 436 val_436 436 val_436 2 +437 val_437 386 val_386 2 437 val_437 437 val_437 2 +438 val_438 155 val_155 2 438 val_438 438 val_438 2 +438 val_438 454 val_454 2 439 val_439 439 val_439 2 +439 val_439 454 val_454 2 +439 val_439 495 val_495 2 44 val_44 NULL NULL 2 +44 val_44 235 val_235 2 44 val_44 44 val_44 2 443 val_443 443 val_443 2 +443 val_443 454 val_454 2 444 val_444 444 val_444 2 +444 val_444 454 val_454 2 446 val_446 446 val_446 2 +446 val_446 495 val_495 2 448 val_448 448 val_448 2 +448 val_448 97 val_97 2 449 val_449 449 val_449 2 +449 val_449 492 val_492 2 +452 val_452 392 val_392 2 452 val_452 452 val_452 2 453 val_453 453 val_453 2 +453 val_453 492 val_492 2 454 val_454 454 val_454 2 +455 val_455 155 val_155 2 455 val_455 455 val_455 2 457 val_457 457 val_457 2 +458 val_458 233 val_233 2 458 val_458 458 val_458 2 +458 val_458 492 val_492 2 +459 val_459 386 val_386 2 459 val_459 459 val_459 2 +459 val_459 495 val_495 2 +460 val_460 333 val_333 2 460 val_460 460 val_460 2 +462 val_462 134 val_134 2 +462 val_462 172 val_172 2 462 val_462 462 val_462 2 +463 val_463 411 val_411 2 463 val_463 463 val_463 2 +466 val_466 235 val_235 2 +466 val_466 353 val_353 2 466 val_466 466 val_466 2 +466 val_466 495 val_495 2 467 val_467 467 val_467 2 +468 val_468 172 val_172 2 +468 val_468 323 val_323 2 468 val_468 468 val_468 2 +469 val_469 386 val_386 2 469 val_469 469 val_469 2 +469 val_469 97 val_97 2 47 val_47 NULL NULL 2 47 val_47 47 val_47 2 470 val_470 470 val_470 2 +472 val_472 233 val_233 2 472 val_472 472 val_472 2 475 val_475 475 val_475 2 +475 val_475 495 val_495 2 477 val_477 477 val_477 2 +477 val_477 97 val_97 2 +478 val_478 323 val_323 2 +478 val_478 454 val_454 2 478 val_478 478 val_478 2 +479 val_479 454 val_454 2 479 val_479 479 val_479 2 +480 val_480 333 val_333 2 +480 val_480 454 val_454 2 480 val_480 480 val_480 2 481 val_481 481 val_481 2 482 val_482 482 val_482 2 +482 val_482 495 val_495 2 +483 val_483 235 val_235 2 483 val_483 483 val_483 2 484 val_484 484 val_484 2 485 val_485 485 val_485 2 487 val_487 487 val_487 2 +489 val_489 170 val_170 2 +489 val_489 233 val_233 2 +489 val_489 333 val_333 2 489 val_489 489 val_489 2 +489 val_489 495 val_495 2 490 val_490 490 val_490 2 +491 val_491 454 val_454 2 491 val_491 491 val_491 2 492 val_492 492 val_492 2 +493 val_493 333 val_333 2 493 val_493 493 val_493 2 494 val_494 494 val_494 2 +494 val_494 495 val_495 2 495 val_495 495 val_495 2 +496 val_496 172 val_172 2 496 val_496 496 val_496 2 +497 val_497 392 val_392 2 497 val_497 497 val_497 2 +498 val_498 233 val_233 2 +498 val_498 492 val_492 2 498 val_498 498 val_498 2 5 val_5 5 val_5 2 +51 val_51 386 val_386 2 51 val_51 51 val_51 2 +53 val_53 235 val_235 2 53 val_53 53 val_53 2 +54 val_54 386 val_386 2 54 val_54 54 val_54 2 +57 val_57 155 val_155 2 57 val_57 57 val_57 2 +58 val_58 353 val_353 2 +58 val_58 392 val_392 2 58 val_58 58 val_58 2 64 val_64 64 val_64 2 +65 val_65 386 val_386 2 65 val_65 65 val_65 2 66 val_66 66 val_66 2 +67 val_67 134 val_134 2 +67 val_67 333 val_333 2 67 val_67 67 val_67 2 69 val_69 69 val_69 2 +70 val_70 454 val_454 2 70 val_70 70 val_70 2 +72 val_72 134 val_134 2 72 val_72 72 val_72 2 74 val_74 74 val_74 2 +76 val_76 233 val_233 2 76 val_76 76 val_76 2 77 val_77 77 val_77 2 +78 val_78 233 val_233 2 78 val_78 78 val_78 2 8 val_8 8 val_8 2 +80 val_80 235 val_235 2 80 val_80 80 val_80 2 +82 val_82 495 val_495 2 82 val_82 82 val_82 2 +83 val_83 333 val_333 2 +83 val_83 386 val_386 2 83 val_83 83 val_83 2 +84 val_84 353 val_353 2 84 val_84 84 val_84 2 +84 val_84 97 val_97 2 85 val_85 85 val_85 2 86 val_86 86 val_86 2 87 val_87 87 val_87 2 9 val_9 9 val_9 2 +90 val_90 134 val_134 2 +90 val_90 257 val_257 2 90 val_90 90 val_90 2 +90 val_90 97 val_97 2 +92 val_92 170 val_170 2 92 val_92 92 val_92 2 +95 val_95 392 val_392 2 95 val_95 95 val_95 2 +96 val_96 454 val_454 2 96 val_96 96 val_96 2 97 val_97 97 val_97 2 98 val_98 98 val_98 2 diff --git ql/src/test/results/clientpositive/tez/vector_aggregate_without_gby.q.out ql/src/test/results/clientpositive/tez/vector_aggregate_without_gby.q.out index 1d84e3b..afe79d3 100644 --- ql/src/test/results/clientpositive/tez/vector_aggregate_without_gby.q.out +++ ql/src/test/results/clientpositive/tez/vector_aggregate_without_gby.q.out @@ -46,12 +46,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_7] compressed:false Statistics:Num rows: 1 Data size: 88 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_12] + Group By Operator [GBY_5] | aggregations:["max(VALUE._col0)","max(VALUE._col1)"] | outputColumnNames:["_col0","_col1"] | Statistics:Num rows: 1 Data size: 88 Basic stats: COMPLETE Column stats: NONE diff --git ql/src/test/results/clientpositive/tez/vector_auto_smb_mapjoin_14.q.out ql/src/test/results/clientpositive/tez/vector_auto_smb_mapjoin_14.q.out index 19ab711..db4df95 100644 --- ql/src/test/results/clientpositive/tez/vector_auto_smb_mapjoin_14.q.out +++ ql/src/test/results/clientpositive/tez/vector_auto_smb_mapjoin_14.q.out @@ -63,12 +63,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_14] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_21] + Group By Operator [GBY_12] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -152,27 +152,27 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 3 vectorized + Reducer 3 File Output Operator [FS_19] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_29] + Group By Operator [GBY_17] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE - |<-Reducer 2 [SIMPLE_EDGE] vectorized + |<-Reducer 2 [SIMPLE_EDGE] Reduce Output Operator [RS_16] sort order: Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions:_col0 (type: bigint) - Group By Operator [OP_28] + Group By Operator [GBY_15] aggregations:["count()"] outputColumnNames:["_col0"] Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE - Select Operator [OP_27] + Select Operator [SEL_13] Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE - Group By Operator [OP_26] + Group By Operator [GBY_12] | keys:KEY._col0 (type: int) | outputColumnNames:["_col0"] | Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE @@ -298,14 +298,14 @@ Stage-0 | keys:{"0":"_col0 (type: int)","1":"_col0 (type: int)"} | outputColumnNames:["_col0","_col1","_col3"] | Statistics:Num rows: 2 Data size: 224 Basic stats: COMPLETE Column stats: NONE - |<-Reducer 2 [SIMPLE_EDGE] vectorized - | Reduce Output Operator [RS_53] + |<-Reducer 2 [SIMPLE_EDGE] + | Reduce Output Operator [RS_29] | key expressions:_col0 (type: int) | Map-reduce partition columns:_col0 (type: int) | sort order:+ | Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE | value expressions:_col1 (type: bigint) - | Group By Operator [OP_52] + | Group By Operator [GBY_12] | | aggregations:["count(VALUE._col0)"] | | keys:KEY._col0 (type: int) | | outputColumnNames:["_col0","_col1"] @@ -346,14 +346,14 @@ Stage-0 | TableScan [TS_0] | alias:a | Statistics:Num rows: 10 Data size: 930 Basic stats: COMPLETE Column stats: NONE - |<-Reducer 6 [SIMPLE_EDGE] vectorized - Reduce Output Operator [RS_55] + |<-Reducer 6 [SIMPLE_EDGE] + Reduce Output Operator [RS_31] key expressions:_col0 (type: int) Map-reduce partition columns:_col0 (type: int) sort order:+ Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE value expressions:_col1 (type: bigint) - Group By Operator [OP_54] + Group By Operator [GBY_26] | aggregations:["count(VALUE._col0)"] | keys:KEY._col0 (type: int) | outputColumnNames:["_col0","_col1"] @@ -466,12 +466,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_16] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_23] + Group By Operator [GBY_14] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -564,12 +564,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_18] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_25] + Group By Operator [GBY_16] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -686,12 +686,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_20] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_27] + Group By Operator [GBY_18] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -798,12 +798,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_16] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_23] + Group By Operator [GBY_14] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -887,12 +887,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 3 vectorized + Reducer 3 File Output Operator [FS_14] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_26] + Group By Operator [GBY_12] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -982,12 +982,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_16] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_23] + Group By Operator [GBY_14] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -1072,12 +1072,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_21] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_34] + Group By Operator [GBY_19] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -1188,12 +1188,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 2 vectorized + Reducer 2 File Output Operator [FS_18] compressed:false Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Group By Operator [OP_25] + Group By Operator [GBY_16] | aggregations:["count(VALUE._col0)"] | outputColumnNames:["_col0"] | Statistics:Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE @@ -1478,15 +1478,15 @@ Stage-4 Stage-3 Dependency Collection{} Stage-2 - Reducer 2 vectorized - File Output Operator [FS_25] + Reducer 2 + File Output Operator [FS_16] compressed:false Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe","name:":"default.dest2"} - Select Operator [OP_24] + Select Operator [SEL_14] outputColumnNames:["_col0","_col1"] Statistics:Num rows: 2 Data size: 204 Basic stats: COMPLETE Column stats: NONE - Group By Operator [OP_23] + Group By Operator [GBY_13] | aggregations:["count(VALUE._col0)"] | keys:KEY._col0 (type: int) | outputColumnNames:["_col0","_col1"] diff --git ql/src/test/results/clientpositive/tez/vector_between_in.q.out ql/src/test/results/clientpositive/tez/vector_between_in.q.out index 11c3d71..304c5d0 100644 --- ql/src/test/results/clientpositive/tez/vector_between_in.q.out +++ ql/src/test/results/clientpositive/tez/vector_between_in.q.out @@ -41,7 +41,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1233808 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date) @@ -97,7 +96,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -151,7 +149,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1233808 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(20,10)) @@ -207,7 +204,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -261,7 +257,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1233808 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date) @@ -314,7 +309,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1233808 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date) @@ -367,7 +361,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1233808 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(20,10)) @@ -423,7 +416,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_binary_join_groupby.q.out ql/src/test/results/clientpositive/tez/vector_binary_join_groupby.q.out index f229697..1f16385 100644 --- ql/src/test/results/clientpositive/tez/vector_binary_join_groupby.q.out +++ ql/src/test/results/clientpositive/tez/vector_binary_join_groupby.q.out @@ -172,7 +172,6 @@ STAGE PLANS: Statistics: Num rows: 50 Data size: 14819 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: tinyint), _col1 (type: smallint), _col2 (type: int), _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: boolean), _col7 (type: string), _col8 (type: timestamp), _col9 (type: decimal(4,2)) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -249,7 +248,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_bucket.q.out ql/src/test/results/clientpositive/tez/vector_bucket.q.out index 0679b49..e218055 100644 --- ql/src/test/results/clientpositive/tez/vector_bucket.q.out +++ ql/src/test/results/clientpositive/tez/vector_bucket.q.out @@ -40,7 +40,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 26 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: string), _col1 (type: string) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: UDFToInteger(VALUE._col0) (type: int), VALUE._col1 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_cast_constant.q.java1.7.out ql/src/test/results/clientpositive/tez/vector_cast_constant.q.java1.7.out index bcbdf06..2a0a57d 100644 --- ql/src/test/results/clientpositive/tez/vector_cast_constant.q.java1.7.out +++ ql/src/test/results/clientpositive/tez/vector_cast_constant.q.java1.7.out @@ -164,7 +164,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: decimal(14,4)) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: decimal(14,4)) diff --git ql/src/test/results/clientpositive/tez/vector_char_2.q.out ql/src/test/results/clientpositive/tez/vector_char_2.q.out index f88ee91..009b46f 100644 --- ql/src/test/results/clientpositive/tez/vector_char_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_char_2.q.out @@ -94,7 +94,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint), _col2 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0), count(VALUE._col1) @@ -109,7 +108,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col1 (type: bigint), _col2 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: char(20)), VALUE._col0 (type: bigint), VALUE._col1 (type: bigint) @@ -223,7 +221,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint), _col2 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0), count(VALUE._col1) @@ -238,7 +235,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col1 (type: bigint), _col2 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: char(20)), VALUE._col0 (type: bigint), VALUE._col1 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vector_char_mapjoin1.q.out ql/src/test/results/clientpositive/tez/vector_char_mapjoin1.q.out index 657f996..d59eb06 100644 --- ql/src/test/results/clientpositive/tez/vector_char_mapjoin1.q.out +++ ql/src/test/results/clientpositive/tez/vector_char_mapjoin1.q.out @@ -185,7 +185,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: char(10)), VALUE._col1 (type: int), VALUE._col2 (type: char(10)) @@ -289,7 +288,6 @@ STAGE PLANS: value expressions: _col1 (type: char(10)), _col2 (type: int), _col3 (type: char(20)) Execution mode: vectorized Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: char(10)), VALUE._col1 (type: int), VALUE._col2 (type: char(20)) @@ -395,7 +393,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: char(10)), VALUE._col1 (type: int), VALUE._col2 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_char_simple.q.out ql/src/test/results/clientpositive/tez/vector_char_simple.q.out index 617620c..2246a53 100644 --- ql/src/test/results/clientpositive/tez/vector_char_simple.q.out +++ ql/src/test/results/clientpositive/tez/vector_char_simple.q.out @@ -83,7 +83,6 @@ STAGE PLANS: value expressions: _col1 (type: char(20)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: char(10)), VALUE._col0 (type: char(20)) @@ -184,7 +183,6 @@ STAGE PLANS: value expressions: _col1 (type: char(20)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: char(10)), VALUE._col0 (type: char(20)) @@ -288,7 +286,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: int) diff --git ql/src/test/results/clientpositive/tez/vector_coalesce.q.out ql/src/test/results/clientpositive/tez/vector_coalesce.q.out index 1142485..6247304 100644 --- ql/src/test/results/clientpositive/tez/vector_coalesce.q.out +++ ql/src/test/results/clientpositive/tez/vector_coalesce.q.out @@ -44,7 +44,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: null (type: double), KEY.reducesinkkey1 (type: string), KEY.reducesinkkey2 (type: int), KEY.reducesinkkey3 (type: float), KEY.reducesinkkey4 (type: smallint), KEY.reducesinkkey5 (type: string) @@ -135,7 +134,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: null (type: tinyint), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: int), KEY.reducesinkkey3 (type: double) @@ -224,7 +222,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: null (type: float), null (type: bigint), 0.0 (type: float) @@ -315,7 +312,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), KEY.reducesinkkey1 (type: timestamp), KEY.reducesinkkey2 (type: timestamp) @@ -404,7 +400,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: null (type: float), null (type: bigint), null (type: float) diff --git ql/src/test/results/clientpositive/tez/vector_coalesce_2.q.out ql/src/test/results/clientpositive/tez/vector_coalesce_2.q.out index 2889273..0e7b1b7 100644 --- ql/src/test/results/clientpositive/tez/vector_coalesce_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_coalesce_2.q.out @@ -202,7 +202,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_count_distinct.q.out ql/src/test/results/clientpositive/tez/vector_count_distinct.q.out index fcc42be..5d6a463 100644 --- ql/src/test/results/clientpositive/tez/vector_count_distinct.q.out +++ ql/src/test/results/clientpositive/tez/vector_count_distinct.q.out @@ -1272,7 +1272,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 3504000 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) @@ -1289,7 +1288,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_custom_udf_configure.q.out ql/src/test/results/clientpositive/tez/vector_custom_udf_configure.q.out new file mode 100644 index 0000000..aea9a9f --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_custom_udf_configure.q.out @@ -0,0 +1,58 @@ +PREHOOK: query: create temporary function UDFHelloTest as 'org.apache.hadoop.hive.ql.exec.vector.UDFHelloTest' +PREHOOK: type: CREATEFUNCTION +PREHOOK: Output: udfhellotest +POSTHOOK: query: create temporary function UDFHelloTest as 'org.apache.hadoop.hive.ql.exec.vector.UDFHelloTest' +POSTHOOK: type: CREATEFUNCTION +POSTHOOK: Output: udfhellotest +PREHOOK: query: create table testorc1(id int, name string) stored as orc +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@testorc1 +POSTHOOK: query: create table testorc1(id int, name string) stored as orc +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@testorc1 +PREHOOK: query: insert into table testorc1 values(1, 'a1'), (2,'a2') +PREHOOK: type: QUERY +PREHOOK: Input: default@values__tmp__table__1 +PREHOOK: Output: default@testorc1 +POSTHOOK: query: insert into table testorc1 values(1, 'a1'), (2,'a2') +POSTHOOK: type: QUERY +POSTHOOK: Input: default@values__tmp__table__1 +POSTHOOK: Output: default@testorc1 +POSTHOOK: Lineage: testorc1.id EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col1, type:string, comment:), ] +POSTHOOK: Lineage: testorc1.name SIMPLE [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col2, type:string, comment:), ] +PREHOOK: query: explain +select id, UDFHelloTest(name) from testorc1 +PREHOOK: type: QUERY +POSTHOOK: query: explain +select id, UDFHelloTest(name) from testorc1 +POSTHOOK: type: QUERY +Plan optimized by CBO. + +Stage-0 + Fetch Operator + limit:-1 + Stage-1 + Map 1 vectorized + File Output Operator [FS_4] + compressed:false + Statistics:Num rows: 2 Data size: 180 Basic stats: COMPLETE Column stats: NONE + table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} + Select Operator [OP_3] + outputColumnNames:["_col0","_col1"] + Statistics:Num rows: 2 Data size: 180 Basic stats: COMPLETE Column stats: NONE + TableScan [TS_0] + alias:testorc1 + Statistics:Num rows: 2 Data size: 180 Basic stats: COMPLETE Column stats: NONE + +PREHOOK: query: select id, UDFHelloTest(name) from testorc1 +PREHOOK: type: QUERY +PREHOOK: Input: default@testorc1 +#### A masked pattern was here #### +POSTHOOK: query: select id, UDFHelloTest(name) from testorc1 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@testorc1 +#### A masked pattern was here #### +1 Hello a1 +2 Hello a2 diff --git ql/src/test/results/clientpositive/tez/vector_data_types.q.out ql/src/test/results/clientpositive/tez/vector_data_types.q.out index 4197666..4660930 100644 --- ql/src/test/results/clientpositive/tez/vector_data_types.q.out +++ ql/src/test/results/clientpositive/tez/vector_data_types.q.out @@ -221,7 +221,6 @@ STAGE PLANS: value expressions: _col3 (type: bigint), _col4 (type: float), _col5 (type: double), _col6 (type: boolean), _col7 (type: string), _col8 (type: timestamp), _col9 (type: decimal(4,2)), _col10 (type: binary) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint), KEY.reducesinkkey1 (type: smallint), KEY.reducesinkkey2 (type: int), VALUE._col0 (type: bigint), VALUE._col1 (type: float), VALUE._col2 (type: double), VALUE._col3 (type: boolean), VALUE._col4 (type: string), VALUE._col5 (type: timestamp), VALUE._col6 (type: decimal(4,2)), VALUE._col7 (type: binary) diff --git ql/src/test/results/clientpositive/tez/vector_date_1.q.out ql/src/test/results/clientpositive/tez/vector_date_1.q.out index 27e10c0..c2564ce 100644 --- ql/src/test/results/clientpositive/tez/vector_date_1.q.out +++ ql/src/test/results/clientpositive/tez/vector_date_1.q.out @@ -103,7 +103,6 @@ STAGE PLANS: value expressions: _col1 (type: date), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: date), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean) @@ -211,7 +210,6 @@ STAGE PLANS: value expressions: _col1 (type: date), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: date), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean) @@ -321,7 +319,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean) @@ -429,7 +426,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean) @@ -542,7 +538,6 @@ STAGE PLANS: value expressions: _col1 (type: date) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: date) @@ -656,7 +651,6 @@ STAGE PLANS: value expressions: _col1 (type: date) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 2001-01-01 (type: date), VALUE._col0 (type: date) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_1.q.out ql/src/test/results/clientpositive/tez/vector_decimal_1.q.out index e9c38b4..b140963 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_1.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_1.q.out @@ -64,7 +64,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean) @@ -125,7 +124,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint) @@ -186,7 +184,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint) @@ -247,7 +244,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) @@ -308,7 +304,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) @@ -369,7 +364,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: float) @@ -430,7 +424,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double) @@ -491,7 +484,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -552,7 +544,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 336 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_10_0.q.out ql/src/test/results/clientpositive/tez/vector_decimal_10_0.q.out index 3bc59ad..7979afb 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_10_0.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_10_0.q.out @@ -64,7 +64,6 @@ STAGE PLANS: Statistics: Num rows: 2 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(10,0)) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_2.q.out ql/src/test/results/clientpositive/tez/vector_decimal_2.q.out index 3de006c..1845a11 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_2.q.out @@ -53,7 +53,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean) @@ -114,7 +113,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint) @@ -175,7 +173,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint) @@ -236,7 +233,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) @@ -297,7 +293,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) @@ -358,7 +353,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: float) @@ -419,7 +413,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double) @@ -480,7 +473,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -552,7 +544,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean) @@ -613,7 +604,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint) @@ -674,7 +664,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint) @@ -735,7 +724,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) @@ -796,7 +784,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) @@ -857,7 +844,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: float) @@ -918,7 +904,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double) @@ -979,7 +964,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -1038,7 +1022,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3.14 (type: decimal(4,2)) @@ -1097,7 +1080,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3.14 (type: decimal(4,2)) @@ -1156,7 +1138,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 1355944339.1234567 (type: decimal(30,8)) @@ -1215,7 +1196,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 1 (type: decimal(10,0)) @@ -1265,7 +1245,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 1 (type: decimal(10,0)) @@ -1324,7 +1303,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3 (type: decimal(10,0)) @@ -1383,7 +1361,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3 (type: decimal(10,0)) @@ -1442,7 +1419,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3 (type: decimal(10,0)) @@ -1501,7 +1477,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 3 (type: decimal(10,0)) @@ -1560,7 +1535,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 1 (type: decimal(20,19)) @@ -1619,7 +1593,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: COMPLETE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: 0.99999999999999999999 (type: decimal(20,20)) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_aggregate.q.out ql/src/test/results/clientpositive/tez/vector_decimal_aggregate.q.out index d10f053..fac2aa4 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_aggregate.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_aggregate.q.out @@ -70,7 +70,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint), _col2 (type: decimal(20,10)), _col3 (type: decimal(20,10)), _col4 (type: decimal(30,10)), _col5 (type: bigint), _col6 (type: decimal(23,14)), _col7 (type: decimal(23,14)), _col8 (type: decimal(33,14)), _col9 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0), max(VALUE._col1), min(VALUE._col2), sum(VALUE._col3), count(VALUE._col4), max(VALUE._col5), min(VALUE._col6), sum(VALUE._col7), count(VALUE._col8) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_expressions.q.out ql/src/test/results/clientpositive/tez/vector_decimal_expressions.q.out index 7532969..f8e7705 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_expressions.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_expressions.q.out @@ -50,7 +50,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(25,14)), KEY.reducesinkkey1 (type: decimal(26,14)), KEY.reducesinkkey2 (type: double), KEY.reducesinkkey3 (type: double), KEY.reducesinkkey4 (type: decimal(12,10)), KEY.reducesinkkey5 (type: int), KEY.reducesinkkey6 (type: smallint), KEY.reducesinkkey7 (type: tinyint), KEY.reducesinkkey8 (type: bigint), KEY.reducesinkkey9 (type: boolean), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: float), KEY.reducesinkkey12 (type: string), KEY.reducesinkkey13 (type: timestamp) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_round.q.out ql/src/test/results/clientpositive/tez/vector_decimal_round.q.out index 9a5d047..78aa994 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_round.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_round.q.out @@ -62,7 +62,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 3 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: decimal(11,0)) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(10,0)), VALUE._col0 (type: decimal(11,0)) @@ -123,7 +122,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 3 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: decimal(10,0)) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: decimal(10,0)), KEY.reducesinkkey0 (type: decimal(11,0)) @@ -212,7 +210,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 3 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: decimal(11,0)) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(10,0)), VALUE._col0 (type: decimal(11,0)) @@ -273,7 +270,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 3 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: decimal(10,0)) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: decimal(10,0)), KEY.reducesinkkey0 (type: decimal(11,0)) @@ -363,7 +359,6 @@ STAGE PLANS: value expressions: _col1 (type: decimal(11,0)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(10,0)), VALUE._col0 (type: decimal(11,0)) @@ -425,7 +420,6 @@ STAGE PLANS: value expressions: _col0 (type: decimal(10,0)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: decimal(10,0)), KEY.reducesinkkey0 (type: decimal(11,0)) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_round_2.q.out ql/src/test/results/clientpositive/tez/vector_decimal_round_2.q.out index 8336999..bb18d5a 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_round_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_round_2.q.out @@ -85,7 +85,6 @@ STAGE PLANS: value expressions: _col1 (type: decimal(21,0)), _col2 (type: decimal(22,1)), _col3 (type: decimal(23,2)), _col4 (type: decimal(24,3)), _col5 (type: decimal(21,0)), _col6 (type: decimal(21,0)), _col7 (type: decimal(21,0)), _col8 (type: decimal(21,0)), _col9 (type: decimal(21,0)), _col10 (type: decimal(21,0)), _col11 (type: decimal(21,0)), _col12 (type: decimal(21,0)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(21,0)), VALUE._col0 (type: decimal(21,0)), VALUE._col1 (type: decimal(22,1)), VALUE._col2 (type: decimal(23,2)), VALUE._col3 (type: decimal(24,3)), VALUE._col4 (type: decimal(21,0)), VALUE._col5 (type: decimal(21,0)), VALUE._col6 (type: decimal(21,0)), VALUE._col7 (type: decimal(21,0)), VALUE._col8 (type: decimal(21,0)), VALUE._col9 (type: decimal(21,0)), VALUE._col10 (type: decimal(21,0)), VALUE._col11 (type: decimal(21,0)) @@ -198,7 +197,6 @@ STAGE PLANS: value expressions: _col1 (type: decimal(21,0)), _col2 (type: decimal(22,1)), _col3 (type: decimal(23,2)), _col4 (type: decimal(24,3)), _col5 (type: decimal(25,4)), _col6 (type: decimal(21,0)), _col7 (type: decimal(21,0)), _col8 (type: decimal(21,0)), _col9 (type: decimal(21,0)), _col10 (type: decimal(21,0)), _col11 (type: decimal(21,0)), _col12 (type: decimal(22,1)), _col13 (type: decimal(23,2)), _col14 (type: decimal(24,3)), _col15 (type: decimal(25,4)), _col16 (type: decimal(21,0)), _col17 (type: decimal(21,0)), _col18 (type: decimal(21,0)), _col19 (type: decimal(21,0)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(21,0)), VALUE._col0 (type: decimal(21,0)), VALUE._col1 (type: decimal(22,1)), VALUE._col2 (type: decimal(23,2)), VALUE._col3 (type: decimal(24,3)), VALUE._col4 (type: decimal(25,4)), VALUE._col5 (type: decimal(21,0)), VALUE._col6 (type: decimal(21,0)), VALUE._col7 (type: decimal(21,0)), VALUE._col8 (type: decimal(21,0)), VALUE._col9 (type: decimal(21,0)), VALUE._col10 (type: decimal(21,0)), VALUE._col11 (type: decimal(22,1)), VALUE._col12 (type: decimal(23,2)), VALUE._col13 (type: decimal(24,3)), VALUE._col14 (type: decimal(25,4)), VALUE._col15 (type: decimal(21,0)), VALUE._col16 (type: decimal(21,0)), VALUE._col17 (type: decimal(21,0)), VALUE._col18 (type: decimal(21,0)) @@ -338,7 +336,6 @@ STAGE PLANS: value expressions: _col1 (type: decimal(21,0)), _col2 (type: decimal(21,0)), _col3 (type: decimal(21,0)), _col4 (type: decimal(21,0)), _col5 (type: decimal(21,0)), _col6 (type: decimal(21,0)), _col7 (type: decimal(21,0)), _col8 (type: decimal(21,0)), _col9 (type: decimal(21,0)), _col10 (type: decimal(21,0)), _col11 (type: decimal(21,0)), _col12 (type: decimal(21,0)), _col13 (type: decimal(21,0)), _col14 (type: decimal(21,0)), _col15 (type: decimal(21,0)), _col16 (type: decimal(21,0)), _col17 (type: decimal(22,1)), _col18 (type: decimal(23,2)), _col19 (type: decimal(24,3)), _col20 (type: decimal(25,4)), _col21 (type: decimal(26,5)), _col22 (type: decimal(27,6)), _col23 (type: decimal(28,7)), _col24 (type: decimal(29,8)), _col25 (type: decimal(30,9)), _col26 (type: decimal(31,10)), _col27 (type: decimal(32,11)), _col28 (type: decimal(33,12)), _col29 (type: decimal(34,13)), _col31 (type: decimal(35,14)), _col32 (type: decimal(36,15)), _col33 (type: decimal(37,16)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(21,0)), VALUE._col0 (type: decimal(21,0)), VALUE._col1 (type: decimal(21,0)), VALUE._col2 (type: decimal(21,0)), VALUE._col3 (type: decimal(21,0)), VALUE._col4 (type: decimal(21,0)), VALUE._col5 (type: decimal(21,0)), VALUE._col6 (type: decimal(21,0)), VALUE._col7 (type: decimal(21,0)), VALUE._col8 (type: decimal(21,0)), VALUE._col9 (type: decimal(21,0)), VALUE._col10 (type: decimal(21,0)), VALUE._col11 (type: decimal(21,0)), VALUE._col12 (type: decimal(21,0)), VALUE._col13 (type: decimal(21,0)), VALUE._col14 (type: decimal(21,0)), VALUE._col15 (type: decimal(21,0)), VALUE._col16 (type: decimal(22,1)), VALUE._col17 (type: decimal(23,2)), VALUE._col18 (type: decimal(24,3)), VALUE._col19 (type: decimal(25,4)), VALUE._col20 (type: decimal(26,5)), VALUE._col21 (type: decimal(27,6)), VALUE._col22 (type: decimal(28,7)), VALUE._col23 (type: decimal(29,8)), VALUE._col24 (type: decimal(30,9)), VALUE._col25 (type: decimal(31,10)), VALUE._col26 (type: decimal(32,11)), VALUE._col27 (type: decimal(33,12)), VALUE._col28 (type: decimal(34,13)), VALUE._col28 (type: decimal(34,13)), VALUE._col29 (type: decimal(35,14)), VALUE._col30 (type: decimal(36,15)), VALUE._col31 (type: decimal(37,16)) @@ -467,7 +464,6 @@ STAGE PLANS: value expressions: _col1 (type: decimal(30,9)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: decimal(30,9)), VALUE._col0 (type: decimal(30,9)), 1809242.315111134 (type: decimal(17,9)), -1809242.315111134 (type: decimal(17,9)) diff --git ql/src/test/results/clientpositive/tez/vector_decimal_udf.q.out ql/src/test/results/clientpositive/tez/vector_decimal_udf.q.out index 6df956d..99ee33d 100644 --- ql/src/test/results/clientpositive/tez/vector_decimal_udf.q.out +++ ql/src/test/results/clientpositive/tez/vector_decimal_udf.q.out @@ -1672,7 +1672,6 @@ STAGE PLANS: Statistics: Num rows: 19 Data size: 2148 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: decimal(38,23)), _col2 (type: decimal(24,14)), _col3 (type: decimal(30,10)) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: decimal(38,23)), VALUE._col1 (type: decimal(24,14)), VALUE._col2 (type: decimal(30,10)) @@ -2567,7 +2566,6 @@ STAGE PLANS: value expressions: _col0 (type: decimal(20,10)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -2634,7 +2632,6 @@ STAGE PLANS: value expressions: _col0 (type: decimal(20,10)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -2701,7 +2698,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_distinct_1.q.out ql/src/test/results/clientpositive/tez/vector_distinct_1.q.out new file mode 100644 index 0000000..faf21c2 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_distinct_1.q.out @@ -0,0 +1,9 @@ +PREHOOK: query: select count(distinct(cint)) from alltypesorc +PREHOOK: type: QUERY +PREHOOK: Input: default@alltypesorc +#### A masked pattern was here #### +POSTHOOK: query: select count(distinct(cint)) from alltypesorc +POSTHOOK: type: QUERY +POSTHOOK: Input: default@alltypesorc +#### A masked pattern was here #### +6082 diff --git ql/src/test/results/clientpositive/tez/vector_distinct_2.q.out ql/src/test/results/clientpositive/tez/vector_distinct_2.q.out index 44d207b..c015b94 100644 --- ql/src/test/results/clientpositive/tez/vector_distinct_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_distinct_2.q.out @@ -143,7 +143,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: tinyint), KEY._col1 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_groupby1.q.out ql/src/test/results/clientpositive/tez/vector_groupby1.q.out new file mode 100644 index 0000000..c731c79 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby1.q.out @@ -0,0 +1,834 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single Long Aggregations +explain +select b, count(*) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single Long Aggregations +explain +select b, count(*) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint) + outputColumnNames: b + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, count(*) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(*) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 1 +-7049618574399692800 1 +-7496839341561954304 1 +-7911421221625077760 1 +-8831091081349758976 1 +-9014145341570203648 1 +1075 1 +1719 1 +1774 1 +2241 1 +2607 1 +302 1 +3118 1 +3764 1 +504 1 +661 1 +7250237407877382144 1 +7432428551399669760 1 +7659279803863146496 1 +7792036342592348160 1 +78 1 +8100036735858401280 1 +8283099811330506752 1 +8895174927321243648 1 +9182828596851990528 1 +PREHOOK: query: select b, min(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, min(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, sum(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, sum(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 1 +-7049618574399692800 1 +-7496839341561954304 1 +-7911421221625077760 1 +-8831091081349758976 1 +-9014145341570203648 1 +1075 1 +1719 1 +1774 1 +2241 1 +2607 1 +302 1 +3118 1 +3764 1 +504 1 +661 1 +7250237407877382144 1 +7432428551399669760 1 +7659279803863146496 1 +7792036342592348160 1 +78 1 +8100036735858401280 1 +8283099811330506752 1 +8895174927321243648 1 +9182828596851990528 1 +PREHOOK: query: select i, count(*) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, count(*) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 1 +-146961490 1 +-1730740504 1 +-1832606512 1 +-1974777102 1 +-2027812975 1 +-407089271 1 +-522450861 1 +-538812082 1 +-772236518 1 +-805288503 1 +-978892011 1 +1008698636 1 +1541249928 1 +174310705 1 +273256071 1 +476704350 1 +737149747 1 +810157660 1 +851975276 1 +868714547 1 +95356298 1 +NULL 3 +PREHOOK: query: select i, min(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, min(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, max(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, max(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, sum(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, sum(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, count(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, count(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 1 +-146961490 1 +-1730740504 1 +-1832606512 1 +-1974777102 1 +-2027812975 1 +-407089271 1 +-522450861 1 +-538812082 1 +-772236518 1 +-805288503 1 +-978892011 1 +1008698636 1 +1541249928 1 +174310705 1 +273256071 1 +476704350 1 +737149747 1 +810157660 1 +851975276 1 +868714547 1 +95356298 1 +NULL 0 +PREHOOK: query: select si, count(*) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, count(*) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 1 +-1928 1 +-21772 1 +-28501 1 +-30304 1 +-31395 1 +-9171 1 +-9609 1 +133 1 +14773 1 +16195 1 +21091 1 +21849 1 +23441 1 +26557 1 +2671 1 +27922 1 +30921 1 +725 1 +7353 1 +7401 1 +8918 1 +NULL 3 +PREHOOK: query: select si, min(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, min(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, max(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, max(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, sum(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, sum(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, count(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, count(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 1 +-1928 1 +-21772 1 +-28501 1 +-30304 1 +-31395 1 +-9171 1 +-9609 1 +133 1 +14773 1 +16195 1 +21091 1 +21849 1 +23441 1 +26557 1 +2671 1 +27922 1 +30921 1 +725 1 +7353 1 +7401 1 +8918 1 +NULL 0 +PREHOOK: query: select t, count(*) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, count(*) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 1 +-19 1 +-29 1 +-35 1 +-4 1 +-43 1 +-46 1 +-57 1 +-58 1 +-87 1 +-92 1 +111 1 +23 1 +31 1 +36 1 +40 1 +47 1 +48 1 +52 1 +54 1 +55 1 +7 1 +73 1 +95 1 +NULL 1 +PREHOOK: query: select t, min(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, min(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, max(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, max(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, sum(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, sum(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, count(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, count(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 1 +-19 1 +-29 1 +-35 1 +-4 1 +-43 1 +-46 1 +-57 1 +-58 1 +-87 1 +-92 1 +111 1 +23 1 +31 1 +36 1 +40 1 +47 1 +48 1 +52 1 +54 1 +55 1 +7 1 +73 1 +95 1 +NULL 0 diff --git ql/src/test/results/clientpositive/tez/vector_groupby10.q.out ql/src/test/results/clientpositive/tez/vector_groupby10.q.out new file mode 100644 index 0000000..306b766 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby10.q.out @@ -0,0 +1,318 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Long Aggregations with String key +explain +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Long Aggregations with String key +explain +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: s (type: string), b (type: bigint) + outputColumnNames: s, b + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(b), max(b), sum(b), count(b) + keys: s (type: string) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: bigint), _col5 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -9080568167841226752 9191943992860327936 7768516223596009040 87 +american history 80 -9175038118837149696 9119046173224370176 -1784196330193843507 78 +biology 91 -9148197394287779840 9116137265342169088 -7961209961346096620 86 +chemistry 79 -9178166810751909888 9091085792947666944 -6666869272136986436 75 +debate 71 -8856821118526734336 9078604269481148416 -7392142879918672005 70 +education 83 -8930307926221807616 9148071980848742400 -7854354868350908468 80 +forestry 76 -9175279464813223936 9194388393453060096 -6596128059684168313 75 +geology 76 -9101953184875757568 9020143715350814720 1913939625854701960 73 +history 74 -9145593811310010368 8987827141270880256 1082457997392013425 71 +industrial engineering 63 -9215144824304721920 9185458640237641728 651091220914778141 61 +joggying 73 -8877053610728161280 8936639033158410240 650723507550745725 70 +kindergarten 61 -9013952631912325120 9207927479837319168 4060088858295423582 58 +linguistics 69 -9203804401302323200 9188173682239275008 -9128943995720928788 63 +mathematics 75 -9075302542655684608 9001907486943993856 -4843070688786362221 73 +nap time 56 -9218875542187065344 9136234417125007360 6818257282609481999 52 +opthamology 75 -9117959922369060864 9131533983989358592 1367237735070318883 71 +philosophy 66 -8710298418608619520 9199741683232399360 4551016788373995594 66 +quiet hour 67 -9008631121684832256 9182828596851990528 5146025034009544543 63 +religion 76 -9213132862973829120 9211455920344088576 2252876202322280954 74 +study skills 77 -9187662685618348032 9132009829414584320 1705035481933188172 75 +topology 69 -9189155542884474880 8900180888218329088 -2309069353931158255 67 +undecided 67 -9157613004431998976 9190466190353661952 4204051913884115722 64 +values clariffication 84 -9210275791460499456 9107991000536498176 -5026363385883231434 77 +wind surfing 90 -9149719074367946752 9169248521377374208 -4552714315226545517 88 +xylophone band 71 -9051477157204770816 9209153648361848832 -1282123898947113698 70 +yard duty 67 -9126793997498957824 9139805788041134080 -5411899933644306220 62 +zync studies 70 -9219066990552760320 9104574294205636608 8089923339479872437 68 +PREHOOK: query: select s, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -2136052026 2144365072 1533003218 89 +american history 80 -2144138362 2051470532 -851194217 75 +biology 91 -2086352100 2045579147 -4969295497 90 +chemistry 79 -2133145181 2111462911 -941014464 76 +debate 71 -2071851852 2044130430 1996299495 68 +education 83 -2027812975 2140632003 13547286481 79 +forestry 76 -2041825946 2097519027 19824997175 72 +geology 76 -2042647152 2068538934 -1405170115 75 +history 74 -2076460151 1844415080 743244477 71 +industrial engineering 63 -2137168636 2114363167 1229906572 58 +joggying 73 -2119724898 2133950868 -1096929419 67 +kindergarten 61 -1988508336 2089198703 -2313595554 58 +linguistics 69 -2096425960 2081152819 -3983960709 65 +mathematics 75 -2117280385 2064448036 -5282605659 69 +nap time 56 -2111312205 2146312499 8624321050 54 +opthamology 75 -2081501748 2069258195 -5533266628 73 +philosophy 66 -2077771325 2124297747 -12421316315 65 +quiet hour 67 -2069439395 2127682701 18873259951 64 +religion 76 -2146432765 2125311222 6681439722 70 +study skills 77 -1955647385 2032271149 10765031439 75 +topology 69 -2147071655 2075919195 2829175314 65 +undecided 67 -2124994385 2102440065 20707906833 65 +values clariffication 84 -2098078720 2142592987 -2202587383 80 +wind surfing 90 -2037628236 1949494660 -8566294168 90 +xylophone band 71 -2138343289 2052773366 -20411810546 70 +yard duty 67 -1983567458 2133492883 568080693 60 +zync studies 70 -2144241640 2144454927 3509622193 65 +PREHOOK: query: select s, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -32491 31923 -441191 89 +american history 80 -32296 32734 99245 75 +biology 91 -32364 32070 113587 86 +chemistry 79 -32755 32019 96819 72 +debate 71 -31596 31390 13677 68 +education 83 -32022 32581 319955 77 +forestry 76 -32266 32589 -73468 68 +geology 76 -32533 30824 -215963 72 +history 74 -31335 31374 -30938 68 +industrial engineering 63 -31395 31706 77646 60 +joggying 73 -31164 32584 77893 70 +kindergarten 61 -31711 32231 -196667 55 +linguistics 69 -31663 32309 50926 65 +mathematics 75 -31998 32210 -137942 69 +nap time 56 -32498 30420 -154592 53 +opthamology 75 -32688 28558 -250546 68 +philosophy 66 -32403 31427 32410 64 +quiet hour 67 -32383 32640 77895 65 +religion 76 -32755 31721 -189965 73 +study skills 77 -32462 32611 234580 73 +topology 69 -30677 32671 156119 65 +undecided 67 -32541 30861 104935 63 +values clariffication 84 -31130 32420 -129978 82 +wind surfing 90 -31182 32547 334213 82 +xylophone band 71 -29907 32212 -47875 67 +yard duty 67 -32240 32563 -217944 63 +zync studies 70 -32119 32669 27391 69 +PREHOOK: query: select s, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -116 123 610 91 +american history 80 -127 123 -806 76 +biology 91 -127 124 -545 88 +chemistry 79 -127 126 -668 75 +debate 71 -127 124 -264 67 +education 83 -127 125 -35 78 +forestry 76 -126 120 623 72 +geology 76 -124 127 -220 74 +history 74 -125 127 297 69 +industrial engineering 63 -124 126 261 62 +joggying 73 -125 125 774 72 +kindergarten 61 -126 127 969 58 +linguistics 69 -127 126 268 67 +mathematics 75 -127 114 -352 72 +nap time 56 -122 118 150 55 +opthamology 75 -122 127 309 73 +philosophy 66 -125 123 -447 61 +quiet hour 67 -127 123 -349 61 +religion 76 -125 124 745 73 +study skills 77 -127 123 704 73 +topology 69 -122 127 418 65 +undecided 67 -120 124 -816 63 +values clariffication 84 -123 127 -291 83 +wind surfing 90 -124 121 667 82 +xylophone band 71 -115 127 505 67 +yard duty 67 -127 110 -975 63 +zync studies 70 -127 120 -956 64 diff --git ql/src/test/results/clientpositive/tez/vector_groupby11.q.out ql/src/test/results/clientpositive/tez/vector_groupby11.q.out new file mode 100644 index 0000000..c7785b1 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby11.q.out @@ -0,0 +1,860 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Multiple Long Key Aggregations (not including min, max, sum) +-- +explain +select b,i,count(*) from vectortab2korc group by b,i +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Multiple Long Key Aggregations (not including min, max, sum) +-- +explain +select b,i,count(*) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: i (type: int), b (type: bigint) + outputColumnNames: i, b + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: i (type: int), b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: int), _col1 (type: bigint) + sort order: ++ + Map-reduce partition columns: _col0 (type: int), _col1 (type: bigint) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col2 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: int), KEY._col1 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col1 (type: bigint), _col0 (type: int), _col2 (type: bigint) + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b,i,count(*) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,count(*) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 1 +-7049618574399692800 -978892011 1 +-7496839341561954304 868714547 1 +-7911421221625077760 476704350 1 +-8831091081349758976 -1832606512 1 +-9014145341570203648 273256071 1 +1075 NULL 1 +1719 1008698636 1 +1774 -1974777102 1 +2241 810157660 1 +2607 NULL 1 +302 -1730740504 1 +3118 NULL 1 +3764 -2027812975 1 +504 851975276 1 +661 -407089271 1 +7250237407877382144 -772236518 1 +7432428551399669760 -805288503 1 +7659279803863146496 1541249928 1 +7792036342592348160 -538812082 1 +78 95356298 1 +8100036735858401280 -146961490 1 +8283099811330506752 737149747 1 +8895174927321243648 -522450861 1 +9182828596851990528 -1012329052 1 +PREHOOK: query: select b,i,min(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,min(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,max(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,max(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,sum(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,sum(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,count(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,count(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 1 +-7049618574399692800 -978892011 1 +-7496839341561954304 868714547 1 +-7911421221625077760 476704350 1 +-8831091081349758976 -1832606512 1 +-9014145341570203648 273256071 1 +1075 NULL 1 +1719 1008698636 1 +1774 -1974777102 1 +2241 810157660 1 +2607 NULL 1 +302 -1730740504 1 +3118 NULL 1 +3764 -2027812975 1 +504 851975276 1 +661 -407089271 1 +7250237407877382144 -772236518 1 +7432428551399669760 -805288503 1 +7659279803863146496 1541249928 1 +7792036342592348160 -538812082 1 +78 95356298 1 +8100036735858401280 -146961490 1 +8283099811330506752 737149747 1 +8895174927321243648 -522450861 1 +9182828596851990528 -1012329052 1 +PREHOOK: query: select i,b,count(*) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,count(*) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 1 +-146961490 8100036735858401280 1 +-1730740504 302 1 +-1832606512 -8831091081349758976 1 +-1974777102 1774 1 +-2027812975 3764 1 +-407089271 661 1 +-522450861 8895174927321243648 1 +-538812082 7792036342592348160 1 +-772236518 7250237407877382144 1 +-805288503 7432428551399669760 1 +-978892011 -7049618574399692800 1 +1008698636 1719 1 +1541249928 7659279803863146496 1 +174310705 -6935038507792801792 1 +273256071 -9014145341570203648 1 +476704350 -7911421221625077760 1 +737149747 8283099811330506752 1 +810157660 2241 1 +851975276 504 1 +868714547 -7496839341561954304 1 +95356298 78 1 +NULL 1075 1 +NULL 2607 1 +NULL 3118 1 +PREHOOK: query: select i,b,min(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,min(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,max(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,max(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,sum(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,sum(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,count(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,count(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 1 +-146961490 8100036735858401280 1 +-1730740504 302 1 +-1832606512 -8831091081349758976 1 +-1974777102 1774 1 +-2027812975 3764 1 +-407089271 661 1 +-522450861 8895174927321243648 1 +-538812082 7792036342592348160 1 +-772236518 7250237407877382144 1 +-805288503 7432428551399669760 1 +-978892011 -7049618574399692800 1 +1008698636 1719 1 +1541249928 7659279803863146496 1 +174310705 -6935038507792801792 1 +273256071 -9014145341570203648 1 +476704350 -7911421221625077760 1 +737149747 8283099811330506752 1 +810157660 2241 1 +851975276 504 1 +868714547 -7496839341561954304 1 +95356298 78 1 +NULL 1075 0 +NULL 2607 0 +NULL 3118 0 +PREHOOK: query: select si,b,count(*) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,count(*) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 1 +-1928 -6935038507792801792 1 +-21772 -8831091081349758976 1 +-28501 2241 1 +-30304 8100036735858401280 1 +-31395 302 1 +-9171 7432428551399669760 1 +-9609 7659279803863146496 1 +133 78 1 +14773 1774 1 +16195 8283099811330506752 1 +21091 9182828596851990528 1 +21849 -7496839341561954304 1 +23441 -7049618574399692800 1 +26557 661 1 +2671 504 1 +27922 3764 1 +30921 8895174927321243648 1 +725 3118 1 +7353 1719 1 +7401 -7911421221625077760 1 +8918 7250237407877382144 1 +NULL -9014145341570203648 1 +NULL 1075 1 +NULL 2607 1 +PREHOOK: query: select si,b,min(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,min(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,max(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,max(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,sum(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,sum(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,count(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,count(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 1 +-1928 -6935038507792801792 1 +-21772 -8831091081349758976 1 +-28501 2241 1 +-30304 8100036735858401280 1 +-31395 302 1 +-9171 7432428551399669760 1 +-9609 7659279803863146496 1 +133 78 1 +14773 1774 1 +16195 8283099811330506752 1 +21091 9182828596851990528 1 +21849 -7496839341561954304 1 +23441 -7049618574399692800 1 +26557 661 1 +2671 504 1 +27922 3764 1 +30921 8895174927321243648 1 +725 3118 1 +7353 1719 1 +7401 -7911421221625077760 1 +8918 7250237407877382144 1 +NULL -9014145341570203648 0 +NULL 1075 0 +NULL 2607 0 +PREHOOK: query: select t,i,count(*) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,count(*) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 1 +-19 95356298 1 +-29 -1974777102 1 +-35 NULL 1 +-4 273256071 1 +-43 851975276 1 +-46 810157660 1 +-57 -522450861 1 +-58 -1730740504 1 +-87 -1012329052 1 +-92 868714547 1 +111 NULL 1 +23 -805288503 1 +31 1541249928 1 +36 -538812082 1 +40 -1832606512 1 +47 -978892011 1 +48 476704350 1 +52 -772236518 1 +54 -146961490 1 +55 174310705 1 +7 NULL 1 +73 737149747 1 +95 -2027812975 1 +NULL -407089271 1 +PREHOOK: query: select t,i,min(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,min(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,max(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,max(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,sum(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,sum(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,count(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,count(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 1 +-19 95356298 1 +-29 -1974777102 1 +-35 NULL 1 +-4 273256071 1 +-43 851975276 1 +-46 810157660 1 +-57 -522450861 1 +-58 -1730740504 1 +-87 -1012329052 1 +-92 868714547 1 +111 NULL 1 +23 -805288503 1 +31 1541249928 1 +36 -538812082 1 +40 -1832606512 1 +47 -978892011 1 +48 476704350 1 +52 -772236518 1 +54 -146961490 1 +55 174310705 1 +7 NULL 1 +73 737149747 1 +95 -2027812975 1 +NULL -407089271 0 diff --git ql/src/test/results/clientpositive/tez/vector_groupby2.q.out ql/src/test/results/clientpositive/tez/vector_groupby2.q.out new file mode 100644 index 0000000..4f53f13 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby2.q.out @@ -0,0 +1,6082 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Long Aggregations +explain +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Long Aggregations +explain +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint) + outputColumnNames: b + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(b), max(b), sum(b), count(b) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: bigint), _col5 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 +-6917607783359897600 1 -6917607783359897600 -6917607783359897600 -6917607783359897600 1 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+POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 c2 c3 c4 c5 +-1001529082 1 -1001529082 -1001529082 -1001529082 1 +-1004204053 1 -1004204053 -1004204053 -1004204053 1 +-1006768637 1 -1006768637 -1006768637 -1006768637 1 +-1009249550 1 -1009249550 -1009249550 -1009249550 1 +-1011125931 1 -1011125931 -1011125931 -1011125931 1 +-1012329052 1 -1012329052 -1012329052 -1012329052 1 +-1017027298 1 -1017027298 -1017027298 -1017027298 1 +-1017629298 1 -1017629298 -1017629298 -1017629298 1 +-101960322 1 -101960322 -101960322 -101960322 1 +-1021859098 1 -1021859098 -1021859098 -1021859098 1 +-1024500955 1 -1024500955 -1024500955 -1024500955 1 +-1026458834 1 -1026458834 -1026458834 -1026458834 1 +-1026746699 1 -1026746699 -1026746699 -1026746699 1 +-1030565036 1 -1030565036 -1030565036 -1030565036 1 +-1031592590 1 -1031592590 -1031592590 -1031592590 1 +-103219371 1 -103219371 -103219371 -103219371 1 +-1032306832 1 -1032306832 -1032306832 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94 94 470 5 +95 7 95 95 665 7 +96 12 96 96 1152 12 +97 9 97 97 873 9 +98 10 98 98 980 10 +99 3 99 99 297 3 +NULL 96 NULL NULL NULL 0 diff --git ql/src/test/results/clientpositive/tez/vector_groupby3.q.out ql/src/test/results/clientpositive/tez/vector_groupby3.q.out new file mode 100644 index 0000000..0d62272 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby3.q.out @@ -0,0 +1,133 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +CREATE TABLE orc_table_1(a INT, b INT) STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@orc_table_1 +POSTHOOK: query: -- SORT_QUERY_RESULTS + +CREATE TABLE orc_table_1(a INT, b INT) STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@orc_table_1 +PREHOOK: query: insert into table orc_table_1 values(12, 1),(5, 1), (9, 2),(12, 1) +PREHOOK: type: QUERY +PREHOOK: Input: default@values__tmp__table__1 +PREHOOK: Output: default@orc_table_1 +POSTHOOK: query: insert into table orc_table_1 values(12, 1),(5, 1), (9, 2),(12, 1) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@values__tmp__table__1 +POSTHOOK: Output: default@orc_table_1 +POSTHOOK: Lineage: orc_table_1.a EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col1, type:string, comment:), ] +POSTHOOK: Lineage: orc_table_1.b EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col2, type:string, comment:), ] +_col0 _col1 +PREHOOK: query: -- +-- 2nd Column Long Aggregations +explain +select b, max(a) from orc_table_1 group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- 2nd Column Long Aggregations +explain +select b, max(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: orc_table_1 + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: int), a (type: int) + outputColumnNames: b, a + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(a) + keys: b (type: int) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: int) + sort order: + + Map-reduce partition columns: _col0 (type: int) + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: int) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + keys: KEY._col0 (type: int) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2 Data size: 16 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 2 Data size: 16 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, min(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, min(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 5 +2 9 +PREHOOK: query: select b, max(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, max(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 12 +2 9 +PREHOOK: query: select b, count(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, count(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 3 +2 1 +PREHOOK: query: select b, sum(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, sum(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 29 +2 9 diff --git ql/src/test/results/clientpositive/tez/vector_groupby4.q.out ql/src/test/results/clientpositive/tez/vector_groupby4.q.out new file mode 100644 index 0000000..39664f4 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby4.q.out @@ -0,0 +1,3896 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Many Column Long Aggregations +explain +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Long Aggregations +explain +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), t (type: tinyint), si (type: smallint), i (type: int) + outputColumnNames: b, t, si, i + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: tinyint), _col2 (type: bigint), _col3 (type: smallint), _col4 (type: smallint), _col5 (type: bigint), _col6 (type: bigint), _col7 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0), sum(VALUE._col1), min(VALUE._col2), max(VALUE._col3), sum(VALUE._col4), count(VALUE._col5), max(VALUE._col6) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 +-6917607783359897600 36 36 -1307 -1307 -603273425 1 -6917607783359897600 +-6919476845891313664 124 124 8811 8811 710856472 1 -6919476845891313664 +-6920172215209426944 115 115 -26832 -26832 -764412063 1 -6920172215209426944 +-6921654334727036928 52 52 21673 21673 -1066775085 1 -6921654334727036928 +-6933565857643814912 -80 -80 NULL NULL 581259902 1 -6933565857643814912 +-6934304742087655424 77 77 NULL NULL 955171928 1 -6934304742087655424 +-6935038507792801792 55 55 -1928 -1928 174310705 1 -6935038507792801792 +-6935548339131138048 -85 -85 24858 24858 -1062159435 1 -6935548339131138048 +-6938706403992854528 -102 -102 30420 30420 980732494 1 -6938706403992854528 +-6941777546186579968 NULL NULL 21611 21611 121663320 1 -6941777546186579968 +-6947955278050181120 -63 -63 16357 16357 641695802 1 -6947955278050181120 +-6951350560260784128 -6 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1805139501 1 961 +965 125 125 26292 26292 1336951982 1 965 +967 -57 -57 11843 11843 -1240208945 1 967 +976 72 72 7058 7058 -1563676282 1 976 +979 123 123 -9798 -9798 1022214896 1 979 +982 -98 -98 -18140 -18140 -835198551 1 982 +987 NULL NULL -19159 -19159 1807877618 1 987 +997 -14 -14 15342 15342 -742707249 1 997 +999 107 107 11159 11159 -346607939 1 999 +NULL 127 -1065 -32371 32563 -9784926725 80 NULL +PREHOOK: query: select b, max(t), sum(t), min(t), min(si), sum(si), max(si), min(i), sum(i), count(i), max(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(t), sum(t), min(t), min(si), sum(si), max(si), min(i), sum(i), count(i), max(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 +-6917607783359897600 36 36 36 -1307 -1307 -1307 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-2069439395 -9784926725 80 2142592987 NULL diff --git ql/src/test/results/clientpositive/tez/vector_groupby5.q.out ql/src/test/results/clientpositive/tez/vector_groupby5.q.out new file mode 100644 index 0000000..89ea67d --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby5.q.out @@ -0,0 +1,514 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single Double Aggregations +explain +select d, count(*) from vectortab2korc group by d +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single Double Aggregations +explain +select d, count(*) from vectortab2korc group by d +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: d (type: double) + outputColumnNames: d + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: d (type: double) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: double) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select d, count(*) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(*) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 1 +-1387292.76 1 +-2081693.61 1 +-2096084.32 1 +-2506000.67 1 +-2636583.28 1 +-2738225.86 1 +-3116102.1 1 +-3860829.99 1 +-3997512.4 1 +-4409416.25 1 +-4926003.8 1 +-497550.81 1 +-51958.2 1 +-642668.92 1 +1118606.84 1 +1666710.2 1 +1848079.75 1 +2121137.53 1 +2409466.9 1 +2883584.5 1 +300734.43 1 +360339.96 1 +3882592.64 1 +393045.55 1 +PREHOOK: query: select d, min(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, min(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, max(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, max(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, sum(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, sum(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 1 +-1387292.76 1 +-2081693.61 1 +-2096084.32 1 +-2506000.67 1 +-2636583.28 1 +-2738225.86 1 +-3116102.1 1 +-3860829.99 1 +-3997512.4 1 +-4409416.25 1 +-4926003.8 1 +-497550.81 1 +-51958.2 1 +-642668.92 1 +1118606.84 1 +1666710.2 1 +1848079.75 1 +2121137.53 1 +2409466.9 1 +2883584.5 1 +300734.43 1 +360339.96 1 +3882592.64 1 +393045.55 1 +PREHOOK: query: select f, count(*) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, count(*) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 1 +-14145.11 1 +-15490.77 1 +-23364.57 1 +-32124.85 1 +-35391.71 1 +-38119.53 1 +-38980.4 1 +-41874.29 1 +-4574.16 1 +-7382.06 1 +-7769.3 1 +11322.18 1 +1466.49 1 +21713.21 1 +22311.9 1 +32666.2 1 +32711.55 1 +35338.95 1 +36810.38 1 +38097.34 1 +4983.6 1 +5758.0 1 +5765.39 1 +8799.89 1 +PREHOOK: query: select f, min(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, min(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.93 +-14145.11 -14145.11 +-15490.77 -15490.77 +-23364.57 -23364.57 +-32124.85 -32124.85 +-35391.71 -35391.71 +-38119.53 -38119.53 +-38980.4 -38980.4 +-41874.29 -41874.29 +-4574.16 -4574.16 +-7382.06 -7382.06 +-7769.3 -7769.3 +11322.18 11322.18 +1466.49 1466.49 +21713.21 21713.21 +22311.9 22311.9 +32666.2 32666.2 +32711.55 32711.55 +35338.95 35338.95 +36810.38 36810.38 +38097.34 38097.34 +4983.6 4983.6 +5758.0 5758.0 +5765.39 5765.39 +8799.89 8799.89 +PREHOOK: query: select f, max(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, max(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.93 +-14145.11 -14145.11 +-15490.77 -15490.77 +-23364.57 -23364.57 +-32124.85 -32124.85 +-35391.71 -35391.71 +-38119.53 -38119.53 +-38980.4 -38980.4 +-41874.29 -41874.29 +-4574.16 -4574.16 +-7382.06 -7382.06 +-7769.3 -7769.3 +11322.18 11322.18 +1466.49 1466.49 +21713.21 21713.21 +22311.9 22311.9 +32666.2 32666.2 +32711.55 32711.55 +35338.95 35338.95 +36810.38 36810.38 +38097.34 38097.34 +4983.6 4983.6 +5758.0 5758.0 +5765.39 5765.39 +8799.89 8799.89 +PREHOOK: query: select f, sum(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, sum(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.9296875 +-14145.11 -14145.1103515625 +-15490.77 -15490.76953125 +-23364.57 -23364.5703125 +-32124.85 -32124.849609375 +-35391.71 -35391.7109375 +-38119.53 -38119.53125 +-38980.4 -38980.3984375 +-41874.29 -41874.2890625 +-4574.16 -4574.16015625 +-7382.06 -7382.06005859375 +-7769.3 -7769.2998046875 +11322.18 11322.1796875 +1466.49 1466.489990234375 +21713.21 21713.2109375 +22311.9 22311.900390625 +32666.2 32666.19921875 +32711.55 32711.55078125 +35338.95 35338.94921875 +36810.38 36810.37890625 +38097.34 38097.33984375 +4983.6 4983.60009765625 +5758.0 5758.0 +5765.39 5765.39013671875 +8799.89 8799.8896484375 +PREHOOK: query: select f, count(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, count(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 1 +-14145.11 1 +-15490.77 1 +-23364.57 1 +-32124.85 1 +-35391.71 1 +-38119.53 1 +-38980.4 1 +-41874.29 1 +-4574.16 1 +-7382.06 1 +-7769.3 1 +11322.18 1 +1466.49 1 +21713.21 1 +22311.9 1 +32666.2 1 +32711.55 1 +35338.95 1 +36810.38 1 +38097.34 1 +4983.6 1 +5758.0 1 +5765.39 1 +8799.89 1 diff --git ql/src/test/results/clientpositive/tez/vector_groupby6.q.out ql/src/test/results/clientpositive/tez/vector_groupby6.q.out new file mode 100644 index 0000000..2cf814d --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby6.q.out @@ -0,0 +1,3997 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Double Aggregations +explain +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Double Aggregations +explain +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: d (type: double) + outputColumnNames: d + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(d), max(d), sum(d), count(d) + keys: d (type: double) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: double), _col3 (type: double), _col4 (type: double), _col5 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: double) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 c2 c3 c4 c5 +-1017367.57 1 -1017367.57 -1017367.57 -1017367.57 1 +-1029930.54 1 -1029930.54 -1029930.54 -1029930.54 1 +-1033805.16 1 -1033805.16 -1033805.16 -1033805.16 1 +-1034617.82 1 -1034617.82 -1034617.82 -1034617.82 1 +-1041376.93 1 -1041376.93 -1041376.93 -1041376.93 1 +-1044012.98 1 -1044012.98 -1044012.98 -1044012.98 1 +-1060105.47 1 -1060105.47 -1060105.47 -1060105.47 1 +-1061360.32 1 -1061360.32 -1061360.32 -1061360.32 1 +-1080682.99 1 -1080682.99 -1080682.99 -1080682.99 1 +-1085683.98 1 -1085683.98 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bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f3 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +_col0 _col1 _col2 _col3 _col4 _col5 _col6 _col7 _col8 _col9 _col10 _col11 _col12 _col13 _col14 _col15 +PREHOOK: query: -- +-- Many Column Double Aggregations +explain +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Double Aggregations +explain +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), d2 (type: double), f (type: float), f2 (type: float), d (type: double) + outputColumnNames: b, d2, f, f2, d + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: float), _col4 (type: float), _col5 (type: double), _col6 (type: bigint), _col7 (type: double) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0), sum(VALUE._col1), min(VALUE._col2), max(VALUE._col3), sum(VALUE._col4), count(VALUE._col5), max(VALUE._col6) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 +-6917607783359897600 -6.9197441481716326E22 -6.9197441481716326E22 20495.55 20495.55 462.239990234375 1 -717022.0 +-6919476845891313664 -6.92161378792563E22 -6.92161378792563E22 45495.65 45495.65 1592.1600341796875 1 -1060105.47 +-6920172215209426944 -6.92230937199465E22 -6.92230937199465E22 17394.03 17394.03 1476.5999755859375 1 561210.22 +-6921654334727036928 -6.9237919492352304E22 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-34858.06 -34858.06 1579.3199462890625 1 -4611759.94 +982 9823032.7106 9823032.7106 39755.57 39755.57 -1258.3199462890625 1 1372628.14 +987 9873048.152099999 9873048.152099999 -5972.19 -5972.19 NULL 0 -119465.75 +997 9973079.0351 9973079.0351 -30055.88 -30055.88 -179.75999450683594 1 3904057.55 +999 9993085.2117 9993085.2117 -24238.72 -24238.72 1373.8800048828125 1 794247.13 +NULL NULL NULL -44985.09 49143.26 -13674.600257873535 83 4997627.14 +PREHOOK: query: select b, max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 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ql/src/test/results/clientpositive/tez/vector_groupby8.q.out @@ -0,0 +1,3905 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f3 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +_col0 _col1 _col2 _col3 _col4 _col5 _col6 _col7 _col8 _col9 _col10 _col11 _col12 _col13 _col14 _col15 +PREHOOK: query: -- +-- Many Column Long, Double Aggregations +explain +select b, min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Long, Double Aggregations +explain +select b, min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), d2 (type: double), f (type: float), f2 (type: float), d (type: double) + outputColumnNames: b, d2, f, f2, d + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: double), _col4 (type: double), _col5 (type: float), _col6 (type: float), _col7 (type: double), _col8 (type: bigint), _col9 (type: double) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0), max(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), min(VALUE._col4), max(VALUE._col5), sum(VALUE._col6), count(VALUE._col7), max(VALUE._col8) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(d2), min(si), sum(d2), min(f), count(t), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(d2), min(si), sum(d2), min(f), count(t), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### 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by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, min(t), max(t), sum(t), count(t), max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 +-6917607783359897600 36 36 36 1 -6.9197441481716326E22 -6.9197441481716326E22 -6.9197441481716326E22 20495.55 20495.55078125 -6.9197441481716326E22 -2636304.8 462.239990234375 1 462.24 -717022.0 +-6919476845891313664 124 124 124 1 -6.92161378792563E22 -6.92161378792563E22 -6.92161378792563E22 45495.65 45495.6484375 -6.92161378792563E22 3106442.8 1592.1600341796875 1 1592.16 -1060105.47 +-6920172215209426944 115 115 115 1 -6.92230937199465E22 -6.92230937199465E22 -6.92230937199465E22 17394.03 17394.029296875 -6.92230937199465E22 -3340480.5 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9973079.0351 9973079.0351 -30055.88 -30055.880859375 9973079.0351 -3245630.8 -179.75999450683594 1 -179.76 3904057.55 +999 107 107 107 1 9993085.2117 9993085.2117 9993085.2117 -24238.72 -24238.720703125 9993085.2117 -1514676.6 1373.8800048828125 1 1373.88 794247.13 +NULL -121 127 -1065 83 NULL NULL NULL -44985.09 -146391.67810058594 NULL -9043450.0 -13674.600257873535 83 1630.68 4997627.14 diff --git ql/src/test/results/clientpositive/tez/vector_groupby9.q.out ql/src/test/results/clientpositive/tez/vector_groupby9.q.out new file mode 100644 index 0000000..9a06565 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vector_groupby9.q.out @@ -0,0 +1,246 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single String Aggregations (not including min, max, sum) +explain +select s, count(*) from vectortab2korc group by s +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single String Aggregations (not including min, max, sum) +explain +select s, count(*) from vectortab2korc group by s +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: s (type: string) + outputColumnNames: s + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: s (type: string) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select s, count(*) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 + 94 +american history 80 +biology 91 +chemistry 79 +debate 71 +education 83 +forestry 76 +geology 76 +history 74 +industrial engineering 63 +joggying 73 +kindergarten 61 +linguistics 69 +mathematics 75 +nap time 56 +opthamology 75 +philosophy 66 +quiet hour 67 +religion 76 +study skills 77 +topology 69 +undecided 67 +values clariffication 84 +wind surfing 90 +xylophone band 71 +yard duty 67 +zync studies 70 +PREHOOK: query: select s, count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 + 87 +american history 78 +biology 86 +chemistry 75 +debate 70 +education 80 +forestry 75 +geology 73 +history 71 +industrial engineering 61 +joggying 70 +kindergarten 58 +linguistics 63 +mathematics 73 +nap time 52 +opthamology 71 +philosophy 66 +quiet hour 63 +religion 74 +study skills 75 +topology 67 +undecided 64 +values clariffication 77 +wind surfing 88 +xylophone band 70 +yard duty 62 +zync studies 68 diff --git ql/src/test/results/clientpositive/tez/vector_groupby_3.q.out ql/src/test/results/clientpositive/tez/vector_groupby_3.q.out index ab2d856..de0124b 100644 --- ql/src/test/results/clientpositive/tez/vector_groupby_3.q.out +++ ql/src/test/results/clientpositive/tez/vector_groupby_3.q.out @@ -145,7 +145,6 @@ STAGE PLANS: value expressions: _col2 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_groupby_reduce.q.out ql/src/test/results/clientpositive/tez/vector_groupby_reduce.q.out index 4322072..c2ece97 100644 --- ql/src/test/results/clientpositive/tez/vector_groupby_reduce.q.out +++ ql/src/test/results/clientpositive/tez/vector_groupby_reduce.q.out @@ -260,7 +260,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) @@ -379,7 +378,6 @@ STAGE PLANS: Statistics: Num rows: 1000 Data size: 88276 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) @@ -401,7 +399,6 @@ STAGE PLANS: sort order: + Statistics: Num rows: 250 Data size: 22069 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int) @@ -589,7 +586,6 @@ STAGE PLANS: value expressions: _col2 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -610,7 +606,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 44138 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: bigint), _col2 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0), sum(VALUE._col1) @@ -624,7 +619,6 @@ STAGE PLANS: Statistics: Num rows: 250 Data size: 22069 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: bigint), _col2 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: bigint), VALUE._col1 (type: bigint) @@ -811,7 +805,6 @@ STAGE PLANS: value expressions: _col2 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -835,7 +828,6 @@ STAGE PLANS: Statistics: Num rows: 250 Data size: 22069 Basic stats: COMPLETE Column stats: NONE value expressions: _col2 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: int), VALUE._col0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vector_grouping_sets.q.out ql/src/test/results/clientpositive/tez/vector_grouping_sets.q.out index afe0187..81f338a 100644 --- ql/src/test/results/clientpositive/tez/vector_grouping_sets.q.out +++ ql/src/test/results/clientpositive/tez/vector_grouping_sets.q.out @@ -220,7 +220,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string), _col1 (type: string) Statistics: Num rows: 24 Data size: 51264 Basic stats: COMPLETE Column stats: NONE Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string), KEY._col1 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_if_expr.q.out ql/src/test/results/clientpositive/tez/vector_if_expr.q.out index 3373fc2..9d975eb 100644 --- ql/src/test/results/clientpositive/tez/vector_if_expr.q.out +++ ql/src/test/results/clientpositive/tez/vector_if_expr.q.out @@ -34,7 +34,6 @@ STAGE PLANS: value expressions: _col1 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), VALUE._col0 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_interval_1.q.out ql/src/test/results/clientpositive/tez/vector_interval_1.q.out index 8072033..b4b2490 100644 --- ql/src/test/results/clientpositive/tez/vector_interval_1.q.out +++ ql/src/test/results/clientpositive/tez/vector_interval_1.q.out @@ -81,7 +81,6 @@ STAGE PLANS: value expressions: _col2 (type: interval_year_month), _col4 (type: interval_day_time) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), 1-2 (type: interval_year_month), VALUE._col1 (type: interval_year_month), 1 02:03:04.000000000 (type: interval_day_time), VALUE._col3 (type: interval_day_time) @@ -170,7 +169,6 @@ STAGE PLANS: value expressions: _col2 (type: interval_year_month), _col3 (type: interval_year_month), _col5 (type: interval_year_month), _col6 (type: interval_year_month) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), 2-4 (type: interval_year_month), VALUE._col1 (type: interval_year_month), VALUE._col2 (type: interval_year_month), 0-0 (type: interval_year_month), VALUE._col4 (type: interval_year_month), VALUE._col5 (type: interval_year_month) @@ -265,7 +263,6 @@ STAGE PLANS: value expressions: _col2 (type: interval_day_time), _col3 (type: interval_day_time), _col5 (type: interval_day_time), _col6 (type: interval_day_time) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), 2 04:06:08.000000000 (type: interval_day_time), VALUE._col1 (type: interval_day_time), VALUE._col2 (type: interval_day_time), 0 00:00:00.000000000 (type: interval_day_time), VALUE._col4 (type: interval_day_time), VALUE._col5 (type: interval_day_time) @@ -374,7 +371,6 @@ STAGE PLANS: value expressions: _col1 (type: date), _col2 (type: date), _col3 (type: date), _col4 (type: date), _col5 (type: date), _col6 (type: date), _col7 (type: timestamp), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: timestamp), _col11 (type: timestamp), _col12 (type: timestamp) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: date), VALUE._col1 (type: date), VALUE._col2 (type: date), VALUE._col3 (type: date), VALUE._col4 (type: date), VALUE._col5 (type: date), VALUE._col6 (type: timestamp), VALUE._col7 (type: timestamp), VALUE._col8 (type: timestamp), VALUE._col9 (type: timestamp), VALUE._col10 (type: timestamp), VALUE._col11 (type: timestamp) @@ -495,7 +491,6 @@ STAGE PLANS: value expressions: _col1 (type: timestamp), _col2 (type: timestamp), _col3 (type: timestamp), _col4 (type: timestamp), _col5 (type: timestamp), _col6 (type: timestamp), _col7 (type: timestamp), _col8 (type: timestamp), _col9 (type: timestamp), _col10 (type: timestamp), _col11 (type: timestamp), _col12 (type: timestamp) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), VALUE._col0 (type: timestamp), VALUE._col1 (type: timestamp), VALUE._col2 (type: timestamp), VALUE._col3 (type: timestamp), VALUE._col4 (type: timestamp), VALUE._col5 (type: timestamp), VALUE._col6 (type: timestamp), VALUE._col7 (type: timestamp), VALUE._col8 (type: timestamp), VALUE._col9 (type: timestamp), VALUE._col10 (type: timestamp), VALUE._col11 (type: timestamp) @@ -598,7 +593,6 @@ STAGE PLANS: value expressions: _col1 (type: interval_day_time), _col2 (type: interval_day_time), _col3 (type: interval_day_time) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), VALUE._col0 (type: interval_day_time), VALUE._col1 (type: interval_day_time), VALUE._col2 (type: interval_day_time) @@ -683,7 +677,6 @@ STAGE PLANS: value expressions: _col1 (type: interval_day_time), _col2 (type: interval_day_time), _col3 (type: interval_day_time) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: interval_day_time), VALUE._col1 (type: interval_day_time), VALUE._col2 (type: interval_day_time) @@ -774,7 +767,6 @@ STAGE PLANS: value expressions: _col1 (type: interval_day_time), _col2 (type: interval_day_time), _col3 (type: interval_day_time), _col4 (type: interval_day_time), _col5 (type: interval_day_time), _col6 (type: interval_day_time) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: interval_day_time), VALUE._col1 (type: interval_day_time), VALUE._col2 (type: interval_day_time), VALUE._col3 (type: interval_day_time), VALUE._col4 (type: interval_day_time), VALUE._col5 (type: interval_day_time) diff --git ql/src/test/results/clientpositive/tez/vector_interval_2.q.out ql/src/test/results/clientpositive/tez/vector_interval_2.q.out index ead4877..243dff6 100644 --- ql/src/test/results/clientpositive/tez/vector_interval_2.q.out +++ ql/src/test/results/clientpositive/tez/vector_interval_2.q.out @@ -137,7 +137,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean), _col19 (type: boolean), _col20 (type: boolean), _col21 (type: boolean), _col22 (type: boolean), _col23 (type: boolean), _col24 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean), VALUE._col9 (type: boolean), VALUE._col10 (type: boolean), VALUE._col11 (type: boolean), VALUE._col12 (type: boolean), VALUE._col13 (type: boolean), VALUE._col14 (type: boolean), VALUE._col15 (type: boolean), VALUE._col16 (type: boolean), VALUE._col17 (type: boolean), VALUE._col18 (type: boolean), VALUE._col19 (type: boolean), VALUE._col20 (type: boolean), VALUE._col21 (type: boolean), VALUE._col22 (type: boolean), VALUE._col23 (type: boolean) @@ -304,7 +303,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col0 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean), VALUE._col9 (type: boolean), VALUE._col5 (type: boolean), VALUE._col10 (type: boolean), VALUE._col11 (type: boolean), VALUE._col12 (type: boolean), VALUE._col13 (type: boolean), VALUE._col14 (type: boolean), VALUE._col10 (type: boolean) @@ -471,7 +469,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean), _col19 (type: boolean), _col20 (type: boolean), _col21 (type: boolean), _col22 (type: boolean), _col23 (type: boolean), _col24 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean), VALUE._col9 (type: boolean), VALUE._col10 (type: boolean), VALUE._col11 (type: boolean), VALUE._col12 (type: boolean), VALUE._col13 (type: boolean), VALUE._col14 (type: boolean), VALUE._col15 (type: boolean), VALUE._col16 (type: boolean), VALUE._col17 (type: boolean), VALUE._col18 (type: boolean), VALUE._col19 (type: boolean), VALUE._col20 (type: boolean), VALUE._col21 (type: boolean), VALUE._col22 (type: boolean), VALUE._col23 (type: boolean) @@ -638,7 +635,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col0 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean), VALUE._col8 (type: boolean), VALUE._col9 (type: boolean), VALUE._col5 (type: boolean), VALUE._col10 (type: boolean), VALUE._col11 (type: boolean), VALUE._col12 (type: boolean), VALUE._col13 (type: boolean), VALUE._col14 (type: boolean), VALUE._col10 (type: boolean) @@ -795,7 +791,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) @@ -947,7 +942,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) @@ -1089,7 +1083,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) @@ -1231,7 +1224,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) @@ -1385,7 +1377,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) @@ -1537,7 +1528,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 394 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp) diff --git ql/src/test/results/clientpositive/tez/vector_join30.q.out ql/src/test/results/clientpositive/tez/vector_join30.q.out index a55ba85..68c8b8e 100644 --- ql/src/test/results/clientpositive/tez/vector_join30.q.out +++ ql/src/test/results/clientpositive/tez/vector_join30.q.out @@ -103,7 +103,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -118,7 +117,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -241,7 +239,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -256,7 +253,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -353,7 +349,6 @@ STAGE PLANS: value expressions: _col0 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -391,7 +386,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -550,7 +544,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -565,7 +558,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -578,7 +570,6 @@ STAGE PLANS: Statistics: Num rows: 250 Data size: 44000 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: string) Reducer 7 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string) @@ -703,7 +694,6 @@ STAGE PLANS: value expressions: _col0 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -736,7 +726,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -751,7 +740,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -764,7 +752,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 88000 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: string) Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string) @@ -889,7 +876,6 @@ STAGE PLANS: value expressions: _col0 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -922,7 +908,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -937,7 +922,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -950,7 +934,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 88000 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: string) Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string) @@ -1075,7 +1058,6 @@ STAGE PLANS: value expressions: _col0 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -1108,7 +1090,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -1123,7 +1104,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -1136,7 +1116,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 88000 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: string) Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string) @@ -1261,7 +1240,6 @@ STAGE PLANS: value expressions: _col0 (type: string) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) @@ -1294,7 +1272,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -1309,7 +1286,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), KEY.reducesinkkey0 (type: string) @@ -1322,7 +1298,6 @@ STAGE PLANS: Statistics: Num rows: 500 Data size: 88000 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: string) Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_left_outer_join.q.out ql/src/test/results/clientpositive/tez/vector_left_outer_join.q.out index b792810..57c0f6b 100644 --- ql/src/test/results/clientpositive/tez/vector_left_outer_join.q.out +++ ql/src/test/results/clientpositive/tez/vector_left_outer_join.q.out @@ -97,7 +97,6 @@ STAGE PLANS: Statistics: Num rows: 12288 Data size: 2641964 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_mapjoin_reduce.q.out ql/src/test/results/clientpositive/tez/vector_mapjoin_reduce.q.out index 5e5d38e..dce64c4 100644 --- ql/src/test/results/clientpositive/tez/vector_mapjoin_reduce.q.out +++ ql/src/test/results/clientpositive/tez/vector_mapjoin_reduce.q.out @@ -120,7 +120,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: int) Statistics: Num rows: 50 Data size: 5999 Basic stats: COMPLETE Column stats: NONE Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) @@ -259,7 +258,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: int) Statistics: Num rows: 50 Data size: 5999 Basic stats: COMPLETE Column stats: NONE Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) diff --git ql/src/test/results/clientpositive/tez/vector_mr_diff_schema_alias.q.out ql/src/test/results/clientpositive/tez/vector_mr_diff_schema_alias.q.out index f60a584..97532bb 100644 --- ql/src/test/results/clientpositive/tez/vector_mr_diff_schema_alias.q.out +++ ql/src/test/results/clientpositive/tez/vector_mr_diff_schema_alias.q.out @@ -351,7 +351,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE value expressions: _col1 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -366,7 +365,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col1 (type: bigint) Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vector_non_string_partition.q.out ql/src/test/results/clientpositive/tez/vector_non_string_partition.q.out index 15e36b5..c2a1d3c 100644 --- ql/src/test/results/clientpositive/tez/vector_non_string_partition.q.out +++ ql/src/test/results/clientpositive/tez/vector_non_string_partition.q.out @@ -62,7 +62,6 @@ STAGE PLANS: value expressions: _col1 (type: tinyint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: tinyint) @@ -139,7 +138,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_orderby_5.q.out ql/src/test/results/clientpositive/tez/vector_orderby_5.q.out index 3788368..3281e53 100644 --- ql/src/test/results/clientpositive/tez/vector_orderby_5.q.out +++ ql/src/test/results/clientpositive/tez/vector_orderby_5.q.out @@ -142,7 +142,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -156,7 +155,6 @@ STAGE PLANS: Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), VALUE._col0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vector_outer_join0.q.out ql/src/test/results/clientpositive/tez/vector_outer_join0.q.out index 21e55dc..0161844 100644 --- ql/src/test/results/clientpositive/tez/vector_outer_join0.q.out +++ ql/src/test/results/clientpositive/tez/vector_outer_join0.q.out @@ -24,6 +24,7 @@ POSTHOOK: Input: default@values__tmp__table__1 POSTHOOK: Output: default@orc_table_1 POSTHOOK: Lineage: orc_table_1.a EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col2, type:string, comment:), ] POSTHOOK: Lineage: orc_table_1.v1 SIMPLE [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col1, type:string, comment:), ] +_col0 _col1 PREHOOK: query: insert into table orc_table_2 values (0, "ZERO"),(2, "TWO"), (3, "THREE"),(null, ""),(4, "FOUR"),(null, "") PREHOOK: type: QUERY PREHOOK: Input: default@values__tmp__table__2 @@ -34,6 +35,7 @@ POSTHOOK: Input: default@values__tmp__table__2 POSTHOOK: Output: default@orc_table_2 POSTHOOK: Lineage: orc_table_2.c EXPRESSION [(values__tmp__table__2)values__tmp__table__2.FieldSchema(name:tmp_values_col1, type:string, comment:), ] POSTHOOK: Lineage: orc_table_2.v2 SIMPLE [(values__tmp__table__2)values__tmp__table__2.FieldSchema(name:tmp_values_col2, type:string, comment:), ] +_col0 _col1 PREHOOK: query: select * from orc_table_1 PREHOOK: type: QUERY PREHOOK: Input: default@orc_table_1 @@ -42,6 +44,7 @@ POSTHOOK: query: select * from orc_table_1 POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 #### A masked pattern was here #### +orc_table_1.v1 orc_table_1.a NULL NULL one 1 @@ -56,6 +59,7 @@ POSTHOOK: query: select * from orc_table_2 POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +orc_table_2.c orc_table_2.v2 0 ZERO 2 TWO 3 THREE @@ -68,6 +72,7 @@ PREHOOK: type: QUERY POSTHOOK: query: explain select t1.v1, t1.a, t2.c, t2.v2 from orc_table_1 t1 left outer join orc_table_2 t2 on t1.a = t2.c POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-1 is a root stage Stage-0 depends on stages: Stage-1 @@ -144,6 +149,7 @@ POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +t1.v1 t1.a t2.c t2.v2 NULL NULL NULL NULL NULL NULL one 1 NULL NULL @@ -156,6 +162,7 @@ PREHOOK: type: QUERY POSTHOOK: query: explain select t1.v1, t1.a, t2.c, t2.v2 from orc_table_1 t1 right outer join orc_table_2 t2 on t1.a = t2.c POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-1 is a root stage Stage-0 depends on stages: Stage-1 @@ -232,6 +239,7 @@ POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +t1.v1 t1.a t2.c t2.v2 NULL NULL 0 ZERO NULL NULL 4 FOUR NULL NULL NULL diff --git ql/src/test/results/clientpositive/tez/vector_outer_join1.q.out ql/src/test/results/clientpositive/tez/vector_outer_join1.q.out index c94c3f2..27bbb47 100644 --- ql/src/test/results/clientpositive/tez/vector_outer_join1.q.out +++ ql/src/test/results/clientpositive/tez/vector_outer_join1.q.out @@ -565,7 +565,6 @@ STAGE PLANS: Statistics: Num rows: 15 Data size: 3651 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0), sum(VALUE._col1) diff --git ql/src/test/results/clientpositive/tez/vector_outer_join2.q.out ql/src/test/results/clientpositive/tez/vector_outer_join2.q.out index 46b09e4..e6acb7d 100644 --- ql/src/test/results/clientpositive/tez/vector_outer_join2.q.out +++ ql/src/test/results/clientpositive/tez/vector_outer_join2.q.out @@ -268,7 +268,6 @@ STAGE PLANS: Statistics: Num rows: 20 Data size: 4431 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0), sum(VALUE._col1) diff --git ql/src/test/results/clientpositive/tez/vector_outer_join4.q.out ql/src/test/results/clientpositive/tez/vector_outer_join4.q.out index f66d5a4..6b0b8ea 100644 --- ql/src/test/results/clientpositive/tez/vector_outer_join4.q.out +++ ql/src/test/results/clientpositive/tez/vector_outer_join4.q.out @@ -934,7 +934,6 @@ STAGE PLANS: Statistics: Num rows: 30 Data size: 7006 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_partition_diff_num_cols.q.out ql/src/test/results/clientpositive/tez/vector_partition_diff_num_cols.q.out index 351444d..0b511d8 100644 --- ql/src/test/results/clientpositive/tez/vector_partition_diff_num_cols.q.out +++ ql/src/test/results/clientpositive/tez/vector_partition_diff_num_cols.q.out @@ -113,7 +113,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -239,7 +238,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -366,7 +364,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -474,7 +471,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -588,7 +584,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_partitioned_date_time.q.out ql/src/test/results/clientpositive/tez/vector_partitioned_date_time.q.out index 500e63e..170b8ea 100644 --- ql/src/test/results/clientpositive/tez/vector_partitioned_date_time.q.out +++ ql/src/test/results/clientpositive/tez/vector_partitioned_date_time.q.out @@ -285,7 +285,6 @@ STAGE PLANS: value expressions: _col0 (type: string), _col1 (type: string), _col3 (type: timestamp), _col4 (type: float) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), KEY.reducesinkkey1 (type: date), VALUE._col2 (type: timestamp), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int) @@ -301,7 +300,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col0 (type: string), _col1 (type: string), _col3 (type: timestamp), _col4 (type: float) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), KEY.reducesinkkey1 (type: date), VALUE._col2 (type: timestamp), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int) @@ -397,7 +395,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1043,7 +1040,6 @@ STAGE PLANS: value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: timestamp), _col3 (type: float) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), VALUE._col2 (type: timestamp), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: date) @@ -1059,7 +1055,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: timestamp), _col3 (type: float) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), VALUE._col2 (type: timestamp), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: date) @@ -1179,7 +1174,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1849,7 +1843,6 @@ STAGE PLANS: value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: date), _col3 (type: float) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), VALUE._col2 (type: date), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: timestamp) @@ -1865,7 +1858,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col0 (type: string), _col1 (type: string), _col2 (type: date), _col3 (type: float) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: string), VALUE._col1 (type: string), VALUE._col2 (type: date), VALUE._col3 (type: float), KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: timestamp) @@ -1985,7 +1977,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vector_reduce1.q.out ql/src/test/results/clientpositive/tez/vector_reduce1.q.out index 6035582..b3cd2f2 100644 --- ql/src/test/results/clientpositive/tez/vector_reduce1.q.out +++ ql/src/test/results/clientpositive/tez/vector_reduce1.q.out @@ -137,7 +137,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vector_reduce2.q.out ql/src/test/results/clientpositive/tez/vector_reduce2.q.out index 81038ff..5a9be44 100644 --- ql/src/test/results/clientpositive/tez/vector_reduce2.q.out +++ ql/src/test/results/clientpositive/tez/vector_reduce2.q.out @@ -137,7 +137,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string), KEY.reducesinkkey1 (type: int), KEY.reducesinkkey2 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_reduce3.q.out ql/src/test/results/clientpositive/tez/vector_reduce3.q.out index 319caef..5ae26e2 100644 --- ql/src/test/results/clientpositive/tez/vector_reduce3.q.out +++ ql/src/test/results/clientpositive/tez/vector_reduce3.q.out @@ -137,7 +137,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_reduce_groupby_decimal.q.out ql/src/test/results/clientpositive/tez/vector_reduce_groupby_decimal.q.out index ec382db..6b6c09e 100644 --- ql/src/test/results/clientpositive/tez/vector_reduce_groupby_decimal.q.out +++ ql/src/test/results/clientpositive/tez/vector_reduce_groupby_decimal.q.out @@ -58,7 +58,6 @@ STAGE PLANS: value expressions: _col4 (type: decimal(20,10)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -73,7 +72,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 value expressions: _col4 (type: decimal(20,10)) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: decimal(20,10)), KEY.reducesinkkey3 (type: decimal(23,14)), VALUE._col0 (type: decimal(20,10)) diff --git ql/src/test/results/clientpositive/tez/vector_string_concat.q.out ql/src/test/results/clientpositive/tez/vector_string_concat.q.out index 1f498a8..60f56ee 100644 --- ql/src/test/results/clientpositive/tez/vector_string_concat.q.out +++ ql/src/test/results/clientpositive/tez/vector_string_concat.q.out @@ -312,7 +312,6 @@ STAGE PLANS: Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -325,7 +324,6 @@ STAGE PLANS: Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.1 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_varchar_mapjoin1.q.out ql/src/test/results/clientpositive/tez/vector_varchar_mapjoin1.q.out index 8ca76db..db074a7 100644 --- ql/src/test/results/clientpositive/tez/vector_varchar_mapjoin1.q.out +++ ql/src/test/results/clientpositive/tez/vector_varchar_mapjoin1.q.out @@ -185,7 +185,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: varchar(10)), VALUE._col1 (type: int), VALUE._col2 (type: varchar(10)) @@ -283,7 +282,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: varchar(10)), VALUE._col1 (type: int), VALUE._col2 (type: varchar(20)) @@ -383,7 +381,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: varchar(10)), VALUE._col1 (type: int), VALUE._col2 (type: string) diff --git ql/src/test/results/clientpositive/tez/vector_varchar_simple.q.out ql/src/test/results/clientpositive/tez/vector_varchar_simple.q.out index cdcc2b0..ed06f3c 100644 --- ql/src/test/results/clientpositive/tez/vector_varchar_simple.q.out +++ ql/src/test/results/clientpositive/tez/vector_varchar_simple.q.out @@ -83,7 +83,6 @@ STAGE PLANS: value expressions: _col1 (type: varchar(20)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: varchar(10)), VALUE._col0 (type: varchar(20)) @@ -184,7 +183,6 @@ STAGE PLANS: value expressions: _col1 (type: varchar(20)) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: varchar(10)), VALUE._col0 (type: varchar(20)) @@ -288,7 +286,6 @@ STAGE PLANS: value expressions: _col0 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: int) diff --git ql/src/test/results/clientpositive/tez/vectorization_0.q.out ql/src/test/results/clientpositive/tez/vectorization_0.q.out index 9c33ace..6d1942c 100644 --- ql/src/test/results/clientpositive/tez/vectorization_0.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_0.q.out @@ -52,7 +52,6 @@ STAGE PLANS: value expressions: _col0 (type: tinyint), _col1 (type: tinyint), _col2 (type: bigint), _col3 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0), max(VALUE._col1), count(VALUE._col2), count(VALUE._col3) @@ -65,7 +64,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 24 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: tinyint), _col2 (type: bigint), _col3 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint), VALUE._col0 (type: tinyint), VALUE._col1 (type: bigint), VALUE._col2 (type: bigint) @@ -146,7 +144,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -158,7 +155,6 @@ STAGE PLANS: sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) @@ -261,7 +257,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 64 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: double), _col4 (type: double), _col5 (type: double), _col6 (type: double), _col7 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: double), VALUE._col4 (type: double), VALUE._col5 (type: double), VALUE._col6 (type: double) @@ -358,7 +353,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint), _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0), max(VALUE._col1), count(VALUE._col2), count(VALUE._col3) @@ -371,7 +365,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 32 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), VALUE._col0 (type: bigint), VALUE._col1 (type: bigint), VALUE._col2 (type: bigint) @@ -452,7 +445,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -464,7 +456,6 @@ STAGE PLANS: sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint) @@ -567,7 +558,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 64 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: double), _col4 (type: double), _col5 (type: double), _col6 (type: double), _col7 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: double), VALUE._col4 (type: double), VALUE._col5 (type: double), VALUE._col6 (type: double) @@ -664,7 +654,6 @@ STAGE PLANS: value expressions: _col0 (type: float), _col1 (type: float), _col2 (type: bigint), _col3 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0), max(VALUE._col1), count(VALUE._col2), count(VALUE._col3) @@ -677,7 +666,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 24 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: float), _col2 (type: bigint), _col3 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: float), VALUE._col0 (type: float), VALUE._col1 (type: bigint), VALUE._col2 (type: bigint) @@ -758,7 +746,6 @@ STAGE PLANS: value expressions: _col0 (type: double) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -770,7 +757,6 @@ STAGE PLANS: sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double) @@ -873,7 +859,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 64 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: double), _col4 (type: double), _col5 (type: double), _col6 (type: double), _col7 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: double), VALUE._col4 (type: double), VALUE._col5 (type: double), VALUE._col6 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_13.q.out ql/src/test/results/clientpositive/tez/vectorization_13.q.out index 04b474e..58a4e15 100644 --- ql/src/test/results/clientpositive/tez/vectorization_13.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_13.q.out @@ -123,7 +123,6 @@ STAGE PLANS: Statistics: Num rows: 1365 Data size: 293479 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.1 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), KEY.reducesinkkey1 (type: tinyint), KEY.reducesinkkey2 (type: timestamp), KEY.reducesinkkey3 (type: float), KEY.reducesinkkey4 (type: string), KEY.reducesinkkey5 (type: tinyint), KEY.reducesinkkey6 (type: tinyint), KEY.reducesinkkey7 (type: tinyint), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey9 (type: double), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: double), KEY.reducesinkkey12 (type: double), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey14 (type: double), KEY.reducesinkkey15 (type: double), KEY.reducesinkkey16 (type: double), KEY.reducesinkkey17 (type: double), KEY.reducesinkkey18 (type: float), KEY.reducesinkkey19 (type: double), KEY.reducesinkkey20 (type: tinyint) @@ -377,7 +376,6 @@ STAGE PLANS: Statistics: Num rows: 1365 Data size: 293479 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.1 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), KEY.reducesinkkey1 (type: tinyint), KEY.reducesinkkey2 (type: timestamp), KEY.reducesinkkey3 (type: float), KEY.reducesinkkey4 (type: string), KEY.reducesinkkey5 (type: tinyint), KEY.reducesinkkey6 (type: tinyint), KEY.reducesinkkey7 (type: tinyint), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey9 (type: double), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: double), KEY.reducesinkkey12 (type: double), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey14 (type: double), KEY.reducesinkkey15 (type: double), KEY.reducesinkkey16 (type: double), KEY.reducesinkkey17 (type: double), KEY.reducesinkkey18 (type: float), KEY.reducesinkkey19 (type: double), KEY.reducesinkkey20 (type: tinyint) diff --git ql/src/test/results/clientpositive/tez/vectorization_14.q.out ql/src/test/results/clientpositive/tez/vectorization_14.q.out index 34144d5..02e3fc6 100644 --- ql/src/test/results/clientpositive/tez/vectorization_14.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_14.q.out @@ -123,7 +123,6 @@ STAGE PLANS: Statistics: Num rows: 303 Data size: 65146 Basic stats: COMPLETE Column stats: NONE value expressions: _col3 (type: boolean), _col5 (type: double), _col6 (type: double), _col7 (type: double), _col8 (type: double), _col9 (type: float), _col10 (type: float), _col11 (type: float), _col12 (type: double), _col13 (type: double), _col14 (type: bigint), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey3 (type: timestamp), KEY.reducesinkkey1 (type: float), KEY.reducesinkkey0 (type: string), VALUE._col0 (type: boolean), KEY.reducesinkkey2 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: double), VALUE._col4 (type: double), VALUE._col5 (type: float), VALUE._col6 (type: float), VALUE._col7 (type: float), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: bigint), VALUE._col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: double), VALUE._col14 (type: double), VALUE._col15 (type: double), VALUE._col16 (type: double), VALUE._col17 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_15.q.out ql/src/test/results/clientpositive/tez/vectorization_15.q.out index dbfdc6a..97227ed 100644 --- ql/src/test/results/clientpositive/tez/vectorization_15.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_15.q.out @@ -119,7 +119,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1320982 Basic stats: COMPLETE Column stats: NONE value expressions: _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: float), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: tinyint), _col16 (type: double), _col17 (type: float), _col18 (type: int), _col19 (type: double), _col20 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: float), KEY.reducesinkkey1 (type: boolean), KEY.reducesinkkey2 (type: double), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: tinyint), KEY.reducesinkkey5 (type: int), KEY.reducesinkkey6 (type: timestamp), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: double), VALUE._col4 (type: float), VALUE._col5 (type: double), VALUE._col6 (type: double), VALUE._col7 (type: double), VALUE._col8 (type: tinyint), VALUE._col9 (type: double), VALUE._col10 (type: float), VALUE._col11 (type: int), VALUE._col12 (type: double), VALUE._col13 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_17.q.out ql/src/test/results/clientpositive/tez/vectorization_17.q.out index de25e7c..7291e96 100644 --- ql/src/test/results/clientpositive/tez/vectorization_17.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_17.q.out @@ -80,7 +80,6 @@ STAGE PLANS: value expressions: _col1 (type: string), _col2 (type: int), _col3 (type: timestamp), _col4 (type: double), _col6 (type: double), _col7 (type: bigint), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey1 (type: float), VALUE._col0 (type: string), VALUE._col1 (type: int), VALUE._col2 (type: timestamp), VALUE._col3 (type: double), KEY.reducesinkkey0 (type: bigint), VALUE._col4 (type: double), VALUE._col5 (type: bigint), VALUE._col6 (type: double), VALUE._col7 (type: double), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: double), VALUE._col11 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_7.q.out ql/src/test/results/clientpositive/tez/vectorization_7.q.out index 897b131..9e6c247 100644 --- ql/src/test/results/clientpositive/tez/vectorization_7.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_7.q.out @@ -86,7 +86,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), KEY.reducesinkkey1 (type: bigint), KEY.reducesinkkey2 (type: smallint), KEY.reducesinkkey3 (type: tinyint), KEY.reducesinkkey4 (type: timestamp), KEY.reducesinkkey5 (type: string), KEY.reducesinkkey6 (type: bigint), KEY.reducesinkkey7 (type: int), KEY.reducesinkkey8 (type: smallint), KEY.reducesinkkey9 (type: tinyint), KEY.reducesinkkey10 (type: int), KEY.reducesinkkey11 (type: bigint), KEY.reducesinkkey12 (type: int), KEY.reducesinkkey9 (type: tinyint), KEY.reducesinkkey14 (type: tinyint) @@ -276,7 +275,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), KEY.reducesinkkey1 (type: bigint), KEY.reducesinkkey2 (type: smallint), KEY.reducesinkkey3 (type: tinyint), KEY.reducesinkkey4 (type: timestamp), KEY.reducesinkkey5 (type: string), KEY.reducesinkkey6 (type: bigint), KEY.reducesinkkey7 (type: int), KEY.reducesinkkey8 (type: smallint), KEY.reducesinkkey9 (type: tinyint), KEY.reducesinkkey10 (type: int), KEY.reducesinkkey11 (type: bigint), KEY.reducesinkkey12 (type: int), KEY.reducesinkkey9 (type: tinyint), KEY.reducesinkkey14 (type: tinyint) diff --git ql/src/test/results/clientpositive/tez/vectorization_8.q.out ql/src/test/results/clientpositive/tez/vectorization_8.q.out index b3a3ce3..9f778b3 100644 --- ql/src/test/results/clientpositive/tez/vectorization_8.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_8.q.out @@ -82,7 +82,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: boolean), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: float), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey6 (type: double), KEY.reducesinkkey7 (type: double), KEY.reducesinkkey8 (type: float), KEY.reducesinkkey9 (type: double), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey11 (type: double), KEY.reducesinkkey12 (type: float), KEY.reducesinkkey13 (type: double) @@ -259,7 +258,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: boolean), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: float), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey6 (type: double), KEY.reducesinkkey7 (type: double), KEY.reducesinkkey8 (type: float), KEY.reducesinkkey9 (type: double), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey11 (type: double), KEY.reducesinkkey12 (type: float), KEY.reducesinkkey13 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_div0.q.out ql/src/test/results/clientpositive/tez/vectorization_div0.q.out index 3472ba1..0166d6b 100644 --- ql/src/test/results/clientpositive/tez/vectorization_div0.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_div0.q.out @@ -176,7 +176,6 @@ STAGE PLANS: value expressions: _col2 (type: double) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), KEY.reducesinkkey1 (type: double), VALUE._col0 (type: double) @@ -352,7 +351,6 @@ STAGE PLANS: value expressions: _col2 (type: double), _col4 (type: double), _col5 (type: double) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double), KEY.reducesinkkey1 (type: double), VALUE._col0 (type: double), KEY.reducesinkkey1 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_limit.q.out ql/src/test/results/clientpositive/tez/vectorization_limit.q.out index 0a943df..c60b69a 100644 --- ql/src/test/results/clientpositive/tez/vectorization_limit.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_limit.q.out @@ -79,7 +79,6 @@ STAGE PLANS: value expressions: _col2 (type: smallint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: tinyint), KEY.reducesinkkey1 (type: double), VALUE._col0 (type: smallint) @@ -266,7 +265,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.3 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: tinyint) @@ -485,7 +483,6 @@ STAGE PLANS: value expressions: _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) @@ -499,7 +496,6 @@ STAGE PLANS: Statistics: Num rows: 3072 Data size: 660491 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.3 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey1 (type: double), KEY.reducesinkkey0 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vectorization_part_project.q.out ql/src/test/results/clientpositive/tez/vectorization_part_project.q.out index a61f391..2bdbadf 100644 --- ql/src/test/results/clientpositive/tez/vectorization_part_project.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_part_project.q.out @@ -77,7 +77,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorization_short_regress.q.out ql/src/test/results/clientpositive/tez/vectorization_short_regress.q.out index db0e78b..dd5c079 100644 --- ql/src/test/results/clientpositive/tez/vectorization_short_regress.q.out +++ ql/src/test/results/clientpositive/tez/vectorization_short_regress.q.out @@ -952,7 +952,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: timestamp), KEY.reducesinkkey3 (type: string), KEY.reducesinkkey4 (type: boolean), KEY.reducesinkkey5 (type: tinyint), KEY.reducesinkkey6 (type: float), KEY.reducesinkkey7 (type: timestamp), KEY.reducesinkkey8 (type: smallint), KEY.reducesinkkey9 (type: bigint), KEY.reducesinkkey10 (type: bigint), KEY.reducesinkkey11 (type: int), KEY.reducesinkkey12 (type: double), KEY.reducesinkkey13 (type: smallint), KEY.reducesinkkey14 (type: smallint), KEY.reducesinkkey15 (type: smallint), KEY.reducesinkkey16 (type: double), KEY.reducesinkkey17 (type: double), KEY.reducesinkkey18 (type: float), KEY.reducesinkkey19 (type: double), KEY.reducesinkkey20 (type: double), KEY.reducesinkkey21 (type: tinyint), KEY.reducesinkkey22 (type: double) @@ -1211,7 +1210,6 @@ STAGE PLANS: TopN Hash Memory Usage: 0.1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: int), KEY.reducesinkkey1 (type: bigint), KEY.reducesinkkey2 (type: string), KEY.reducesinkkey3 (type: boolean), KEY.reducesinkkey4 (type: float), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey6 (type: timestamp), KEY.reducesinkkey7 (type: smallint), KEY.reducesinkkey8 (type: string), KEY.reducesinkkey9 (type: boolean), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: double), KEY.reducesinkkey12 (type: double), KEY.reducesinkkey13 (type: double), KEY.reducesinkkey14 (type: float), KEY.reducesinkkey15 (type: float), KEY.reducesinkkey16 (type: float), KEY.reducesinkkey17 (type: double), KEY.reducesinkkey18 (type: double), KEY.reducesinkkey19 (type: bigint), KEY.reducesinkkey20 (type: double), KEY.reducesinkkey21 (type: smallint), KEY.reducesinkkey22 (type: bigint), KEY.reducesinkkey23 (type: double), KEY.reducesinkkey21 (type: smallint) @@ -1420,7 +1418,6 @@ STAGE PLANS: value expressions: _col2 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey7 (type: int), KEY.reducesinkkey1 (type: string), VALUE._col0 (type: boolean), KEY.reducesinkkey2 (type: timestamp), KEY.reducesinkkey6 (type: double), KEY.reducesinkkey3 (type: float), KEY.reducesinkkey4 (type: bigint), KEY.reducesinkkey8 (type: smallint), KEY.reducesinkkey0 (type: boolean), KEY.reducesinkkey10 (type: int), KEY.reducesinkkey11 (type: bigint), KEY.reducesinkkey12 (type: bigint), KEY.reducesinkkey13 (type: float), KEY.reducesinkkey14 (type: bigint), KEY.reducesinkkey15 (type: double), KEY.reducesinkkey16 (type: double), KEY.reducesinkkey17 (type: bigint), KEY.reducesinkkey18 (type: double), KEY.reducesinkkey19 (type: double), KEY.reducesinkkey20 (type: double), KEY.reducesinkkey21 (type: smallint), KEY.reducesinkkey22 (type: int) @@ -1687,7 +1684,6 @@ STAGE PLANS: value expressions: _col0 (type: timestamp) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: VALUE._col0 (type: timestamp), KEY.reducesinkkey1 (type: string), KEY.reducesinkkey2 (type: double), KEY.reducesinkkey3 (type: float), KEY.reducesinkkey4 (type: bigint), KEY.reducesinkkey0 (type: smallint), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey6 (type: int), KEY.reducesinkkey7 (type: float), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey9 (type: double), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: float), KEY.reducesinkkey12 (type: int), KEY.reducesinkkey8 (type: double), KEY.reducesinkkey14 (type: double) @@ -1919,7 +1915,6 @@ STAGE PLANS: Statistics: Num rows: 1251 Data size: 268968 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.1 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: smallint), KEY.reducesinkkey1 (type: int), KEY.reducesinkkey2 (type: double), KEY.reducesinkkey3 (type: double), KEY.reducesinkkey4 (type: bigint), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey6 (type: int), KEY.reducesinkkey7 (type: double), KEY.reducesinkkey8 (type: int), KEY.reducesinkkey9 (type: bigint), KEY.reducesinkkey10 (type: bigint) @@ -2127,7 +2122,6 @@ STAGE PLANS: Statistics: Num rows: 1327 Data size: 285309 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: double), _col4 (type: bigint), _col5 (type: double), _col6 (type: double), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: double), VALUE._col0 (type: double), VALUE._col1 (type: double), VALUE._col2 (type: double), VALUE._col3 (type: bigint), VALUE._col4 (type: double), VALUE._col5 (type: double), VALUE._col6 (type: double), VALUE._col7 (type: double), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: double), VALUE._col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: double), VALUE._col12 (type: double) @@ -2382,7 +2376,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1320982 Basic stats: COMPLETE Column stats: NONE TopN Hash Memory Usage: 0.1 Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: timestamp), KEY.reducesinkkey1 (type: string), KEY.reducesinkkey2 (type: double), KEY.reducesinkkey3 (type: double), KEY.reducesinkkey4 (type: double), KEY.reducesinkkey5 (type: double), KEY.reducesinkkey4 (type: double), KEY.reducesinkkey7 (type: double), KEY.reducesinkkey8 (type: bigint), KEY.reducesinkkey9 (type: bigint), KEY.reducesinkkey10 (type: double), KEY.reducesinkkey11 (type: tinyint), KEY.reducesinkkey12 (type: double), KEY.reducesinkkey13 (type: double), KEY.reducesinkkey14 (type: double), KEY.reducesinkkey15 (type: double), KEY.reducesinkkey16 (type: double), KEY.reducesinkkey17 (type: double), KEY.reducesinkkey18 (type: double), KEY.reducesinkkey19 (type: double), KEY.reducesinkkey20 (type: double), KEY.reducesinkkey21 (type: double), KEY.reducesinkkey22 (type: double), KEY.reducesinkkey23 (type: double), KEY.reducesinkkey24 (type: double), KEY.reducesinkkey25 (type: double), KEY.reducesinkkey26 (type: double), KEY.reducesinkkey27 (type: tinyint), KEY.reducesinkkey28 (type: double), KEY.reducesinkkey29 (type: double), KEY.reducesinkkey30 (type: double), KEY.reducesinkkey31 (type: double), KEY.reducesinkkey32 (type: double), KEY.reducesinkkey33 (type: double), KEY.reducesinkkey34 (type: bigint), KEY.reducesinkkey35 (type: double), KEY.reducesinkkey36 (type: bigint), KEY.reducesinkkey8 (type: bigint), KEY.reducesinkkey38 (type: double) @@ -2715,7 +2708,6 @@ STAGE PLANS: Statistics: Num rows: 2389 Data size: 513643 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: float), _col2 (type: float), _col3 (type: double), _col4 (type: bigint), _col5 (type: double), _col6 (type: double), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: bigint), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col17 (type: bigint), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), VALUE._col0 (type: float), VALUE._col1 (type: float), VALUE._col2 (type: double), VALUE._col3 (type: bigint), VALUE._col4 (type: double), VALUE._col5 (type: double), VALUE._col6 (type: double), VALUE._col7 (type: double), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: bigint), VALUE._col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: double), VALUE._col14 (type: double), VALUE._col12 (type: double), VALUE._col15 (type: bigint), VALUE._col16 (type: double), VALUE._col17 (type: double), VALUE._col18 (type: double), VALUE._col19 (type: double), VALUE._col20 (type: double), VALUE._col21 (type: double), VALUE._col22 (type: double), VALUE._col23 (type: double) @@ -2867,7 +2859,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2934,7 +2925,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3045,7 +3035,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3112,7 +3101,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3179,7 +3167,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3246,7 +3233,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3313,7 +3299,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3380,7 +3365,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vectorized_date_funcs.q.out ql/src/test/results/clientpositive/tez/vectorized_date_funcs.q.out index de7c2ef..ee8114a 100644 --- ql/src/test/results/clientpositive/tez/vectorized_date_funcs.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_date_funcs.q.out @@ -963,7 +963,6 @@ STAGE PLANS: value expressions: _col0 (type: date), _col1 (type: date), _col2 (type: bigint), _col3 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0), max(VALUE._col1), count(VALUE._col2), count(VALUE._col3) @@ -976,7 +975,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 128 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: date), _col2 (type: bigint), _col3 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: date), VALUE._col0 (type: date), VALUE._col1 (type: bigint), VALUE._col2 (type: bigint) diff --git ql/src/test/results/clientpositive/tez/vectorized_distinct_gby.q.out ql/src/test/results/clientpositive/tez/vectorized_distinct_gby.q.out index db604f8..c84ae11 100644 --- ql/src/test/results/clientpositive/tez/vectorized_distinct_gby.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_distinct_gby.q.out @@ -56,7 +56,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint), _col1 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0), count(VALUE._col1) @@ -123,7 +122,6 @@ STAGE PLANS: Statistics: Num rows: 12288 Data size: 2641964 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: int) diff --git ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out index a381309..7028821 100644 --- ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out @@ -72,7 +72,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string) Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -271,7 +270,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -380,7 +378,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -571,7 +568,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -722,7 +718,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -877,7 +872,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -986,7 +980,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1123,7 +1116,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1232,7 +1224,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1370,7 +1361,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1497,7 +1487,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1609,7 +1598,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1721,7 +1709,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1861,7 +1848,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1984,7 +1970,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -1999,7 +1984,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -2120,7 +2104,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2261,7 +2244,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2387,7 +2369,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2492,7 +2473,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2599,7 +2579,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2740,7 +2719,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -2887,7 +2865,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3013,7 +2990,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3028,7 +3004,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -3064,7 +3039,6 @@ STAGE PLANS: Target column: ds Target Vertex: Map 1 Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -3215,7 +3189,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string) Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -3230,7 +3203,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 5 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -3266,7 +3238,6 @@ STAGE PLANS: Target column: ds Target Vertex: Map 1 Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -3416,7 +3387,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: string) Reducer 11 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) @@ -3467,7 +3437,6 @@ STAGE PLANS: Target column: ds Target Vertex: Map 5 Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -3497,7 +3466,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 6 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -3510,7 +3478,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string) Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE Reducer 8 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -3673,7 +3640,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -3854,7 +3820,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4005,7 +3970,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4138,7 +4102,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4250,7 +4213,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4373,7 +4335,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4477,7 +4438,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string) Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -4505,7 +4465,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: bigint) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4635,7 +4594,6 @@ STAGE PLANS: Target Vertex: Map 1 Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4721,7 +4679,6 @@ STAGE PLANS: Map-reduce partition columns: _col0 (type: string) Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4809,7 +4766,6 @@ STAGE PLANS: value expressions: _col0 (type: bigint) Execution mode: vectorized Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -4941,7 +4897,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -5079,7 +5034,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: count(VALUE._col0) @@ -5196,7 +5150,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: string) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator keys: KEY._col0 (type: string) @@ -5211,7 +5164,6 @@ STAGE PLANS: output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: max(VALUE._col0) @@ -5247,7 +5199,6 @@ STAGE PLANS: Target column: ds Target Vertex: Map 1 Reducer 7 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out.orig ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out.orig new file mode 100644 index 0000000..a381309 --- /dev/null +++ ql/src/test/results/clientpositive/tez/vectorized_dynamic_partition_pruning.q.out.orig @@ -0,0 +1,5537 @@ +PREHOOK: query: select distinct ds from srcpart +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select distinct ds from srcpart +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +2008-04-08 +2008-04-09 +PREHOOK: query: select distinct hr from srcpart +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select distinct hr from srcpart +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +11 +12 +PREHOOK: query: EXPLAIN create table srcpart_date as select ds as ds, ds as `date` from srcpart group by ds +PREHOOK: type: CREATETABLE_AS_SELECT +POSTHOOK: query: EXPLAIN create table srcpart_date as select ds as ds, ds as `date` from srcpart group by ds +POSTHOOK: type: CREATETABLE_AS_SELECT +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-2 depends on stages: Stage-1 + Stage-4 depends on stages: Stage-2, Stage-0 + Stage-3 depends on stages: Stage-4 + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: ds (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string), _col0 (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + name: default.srcpart_date + + Stage: Stage-2 + Dependency Collection + + Stage: Stage-4 + Create Table Operator: + Create Table + columns: ds string, date string + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat + serde name: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + name: default.srcpart_date + + Stage: Stage-3 + Stats-Aggr Operator + + Stage: Stage-0 + Move Operator + files: + hdfs directory: true +#### A masked pattern was here #### + +PREHOOK: query: create table srcpart_date stored as orc as select ds as ds, ds as `date` from srcpart group by ds +PREHOOK: type: CREATETABLE_AS_SELECT +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Output: database:default +PREHOOK: Output: default@srcpart_date +POSTHOOK: query: create table srcpart_date stored as orc as select ds as ds, ds as `date` from srcpart group by ds +POSTHOOK: type: CREATETABLE_AS_SELECT +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Output: database:default +POSTHOOK: Output: default@srcpart_date +PREHOOK: query: create table srcpart_hour stored as orc as select hr as hr, hr as hour from srcpart group by hr +PREHOOK: type: CREATETABLE_AS_SELECT +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Output: database:default +PREHOOK: Output: default@srcpart_hour +POSTHOOK: query: create table srcpart_hour stored as orc as select hr as hr, hr as hour from srcpart group by hr +POSTHOOK: type: CREATETABLE_AS_SELECT +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Output: database:default +POSTHOOK: Output: default@srcpart_hour +PREHOOK: query: create table srcpart_date_hour stored as orc as select ds as ds, ds as `date`, hr as hr, hr as hour from srcpart group by ds, hr +PREHOOK: type: CREATETABLE_AS_SELECT +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Output: database:default +PREHOOK: Output: default@srcpart_date_hour +POSTHOOK: query: create table srcpart_date_hour stored as orc as select ds as ds, ds as `date`, hr as hr, hr as hour from srcpart group by ds, hr +POSTHOOK: type: CREATETABLE_AS_SELECT +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Output: database:default +POSTHOOK: Output: default@srcpart_date_hour +PREHOOK: query: create table srcpart_double_hour stored as orc as select (hr*2) as hr, hr as hour from srcpart group by hr +PREHOOK: type: CREATETABLE_AS_SELECT +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Output: database:default +PREHOOK: Output: default@srcpart_double_hour +POSTHOOK: query: create table srcpart_double_hour stored as orc as select (hr*2) as hr, hr as hour from srcpart group by hr +POSTHOOK: type: CREATETABLE_AS_SELECT +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Output: database:default +POSTHOOK: Output: default@srcpart_double_hour +PREHOOK: query: -- single column, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- single column, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: -- multiple sources, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- multiple sources, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 5 (SIMPLE_EDGE) + Reducer 3 <- Map 6 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) + Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: string) + Map 5 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Map 6 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: hr + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col1 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col1 (type: string) + sort order: + + Map-reduce partition columns: _col1 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reducer 3 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col1 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2420 Data size: 25709 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +500 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 5 (SIMPLE_EDGE) + Reducer 3 <- Map 6 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) + Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds is not null and hr is not null) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: string) + Map 5 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Map 6 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col1 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col1 (type: string) + sort order: + + Map-reduce partition columns: _col1 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reducer 3 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col1 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2420 Data size: 25709 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +500 +PREHOOK: query: select count(*) from srcpart where hr = 11 and ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where hr = 11 and ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +500 +PREHOOK: query: -- multiple columns single source +EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- multiple columns single source +EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col1 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col1 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col2 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: _col2 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: hr + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string), _col1 (type: string) + 1 _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +500 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds is not null and hr is not null) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col1 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col1 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col2 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string), _col1 (type: string) + 1 _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +500 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' and hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' and hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +500 +PREHOOK: query: -- empty set +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +PREHOOK: type: QUERY +POSTHOOK: query: -- empty set +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +0 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +0 +PREHOOK: query: select count(*) from srcpart where ds = 'I DONT EXIST' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = 'I DONT EXIST' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +#### A masked pattern was here #### +0 +PREHOOK: query: -- expressions +EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- expressions +EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToDouble(_col0) (type: double) + sort order: + + Map-reduce partition columns: UDFToDouble(_col0) (type: double) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + sort order: + + Map-reduce partition columns: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: double) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: UDFToDouble(hr) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 UDFToDouble(_col0) (type: double) + 1 UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: (UDFToDouble(_col0) * 2.0) (type: double) + sort order: + + Map-reduce partition columns: (UDFToDouble(_col0) * 2.0) (type: double) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: double) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: (UDFToDouble(hr) * 2.0) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 (UDFToDouble(_col0) * 2.0) (type: double) + 1 _col0 (type: double) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToDouble(_col0) (type: double) + sort order: + + Map-reduce partition columns: UDFToDouble(_col0) (type: double) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + sort order: + + Map-reduce partition columns: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 UDFToDouble(_col0) (type: double) + 1 UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: (UDFToDouble(_col0) * 2.0) (type: double) + sort order: + + Map-reduce partition columns: (UDFToDouble(_col0) * 2.0) (type: double) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 (UDFToDouble(_col0) * 2.0) (type: double) + 1 _col0 (type: double) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (cast(srcpart.hr*2 as string) = cast(srcpart_double_hour.hr as string)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (cast(srcpart.hr*2 as string) = cast(srcpart_double_hour.hr as string)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: UDFToString((UDFToDouble(hr) * 2.0)) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: UDFToString((UDFToDouble(hr) * 2.0)) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToString((UDFToDouble(_col0) * 2.0)) (type: string) + sort order: + + Map-reduce partition columns: UDFToString((UDFToDouble(_col0) * 2.0)) (type: string) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and UDFToString(hr) is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and UDFToString(hr) is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToString(_col0) (type: string) + sort order: + + Map-reduce partition columns: UDFToString(_col0) (type: string) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: UDFToString(_col0) (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: UDFToString((UDFToDouble(hr) * 2.0)) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 UDFToString((UDFToDouble(_col0) * 2.0)) (type: string) + 1 UDFToString(_col0) (type: string) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (cast(srcpart.hr*2 as string) = cast(srcpart_double_hour.hr as string)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (cast(srcpart.hr*2 as string) = cast(srcpart_double_hour.hr as string)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where cast(hr as string) = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where cast(hr as string) = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +1000 +Warning: Shuffle Join MERGEJOIN[21][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product +PREHOOK: query: -- parent is reduce tasks +EXPLAIN select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- parent is reduce tasks +EXPLAIN select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) + Reducer 5 <- Map 4 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds = '2008-04-08') (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds = '2008-04-08') (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: '2008-04-08' (type: string) + outputColumnNames: ds + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: ds (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 + 1 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + Reducer 5 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +Warning: Shuffle Join MERGEJOIN[21][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product +PREHOOK: query: select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +Warning: Shuffle Join MERGEJOIN[19][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product +PREHOOK: query: -- non-equi join +EXPLAIN select count(*) from srcpart, srcpart_date_hour where (srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11) and (srcpart.ds = srcpart_date_hour.ds or srcpart.hr = srcpart_date_hour.hr) +PREHOOK: type: QUERY +POSTHOOK: query: -- non-equi join +EXPLAIN select count(*) from srcpart, srcpart_date_hour where (srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11) and (srcpart.ds = srcpart_date_hour.ds or srcpart.hr = srcpart_date_hour.hr) +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string), _col1 (type: string) + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: ((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string), _col2 (type: string) + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 + 1 + outputColumnNames: _col0, _col1, _col2, _col4 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((_col0 = _col2) or (_col1 = _col4)) (type: boolean) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +Warning: Shuffle Join MERGEJOIN[19][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product +PREHOOK: query: select count(*) from srcpart, srcpart_date_hour where (srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11) and (srcpart.ds = srcpart_date_hour.ds or srcpart.hr = srcpart_date_hour.hr) +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart, srcpart_date_hour where (srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11) and (srcpart.ds = srcpart_date_hour.ds or srcpart.hr = srcpart_date_hour.hr) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +1500 +PREHOOK: query: -- old style join syntax +EXPLAIN select count(*) from srcpart, srcpart_date_hour where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 and srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr +PREHOOK: type: QUERY +POSTHOOK: query: -- old style join syntax +EXPLAIN select count(*) from srcpart, srcpart_date_hour where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 and srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col1 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col1 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col2 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: _col2 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: hr + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string), _col1 (type: string) + 1 _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart, srcpart_date_hour where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 and srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart, srcpart_date_hour where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 and srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +500 +PREHOOK: query: -- left join +EXPLAIN select count(*) from srcpart left join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- left join +EXPLAIN select count(*) from srcpart left join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: EXPLAIN select count(*) from srcpart_date left join srcpart on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart_date left join srcpart on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 4 + Execution mode: vectorized + Map 4 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[5]) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Left Outer Join0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- full outer +EXPLAIN select count(*) from srcpart full outer join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- full outer +EXPLAIN select count(*) from srcpart full outer join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[6]) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Right Outer Join0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- with static pruning +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- with static pruning +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 5 (SIMPLE_EDGE) + Reducer 3 <- Map 6 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) + Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[14]) (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 5 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Map 6 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and (UDFToDouble(hr) = 11.0)) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and (UDFToDouble(hr) = 11.0)) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: '11' (type: string) + sort order: + + Map-reduce partition columns: '11' (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: '11' (type: string) + sort order: + + Map-reduce partition columns: '11' (type: string) + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Reducer 3 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 '11' (type: string) + 1 '11' (type: string) + Statistics: Num rows: 1210 Data size: 12854 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +500 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 5 (SIMPLE_EDGE) + Reducer 3 <- Map 6 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) + Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ((UDFToDouble(hr) = 13.0) and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hr) = 13.0) and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Execution mode: vectorized + Map 5 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Map 6 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: (UDFToDouble(hr) = 13.0) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (UDFToDouble(hr) = 13.0) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: '13' (type: string) + sort order: + + Map-reduce partition columns: '13' (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Reduce Output Operator + key expressions: '13' (type: string) + sort order: + + Map-reduce partition columns: '13' (type: string) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Reducer 3 + Reduce Operator Tree: + Merge Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 '13' (type: string) + 1 '13' (type: string) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +0 +PREHOOK: query: -- union + subquery +EXPLAIN select count(*) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +POSTHOOK: query: -- union + subquery +EXPLAIN select count(*) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Union 6 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) + Reducer 5 <- Map 4 (SIMPLE_EDGE), Union 6 (CONTAINS) + Reducer 8 <- Map 7 (SIMPLE_EDGE), Union 6 (CONTAINS) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Map 7 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Left Semi Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + Reducer 5 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Reducer 8 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Union 6 + Vertex: Union 6 + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +2000 +PREHOOK: query: EXPLAIN select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 2 <- Map 1 (SIMPLE_EDGE), Union 6 (SIMPLE_EDGE) + Reducer 3 <- Reducer 2 (SIMPLE_EDGE) + Reducer 5 <- Map 4 (SIMPLE_EDGE), Union 6 (CONTAINS) + Reducer 8 <- Map 7 (SIMPLE_EDGE), Union 6 (CONTAINS) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 4 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Map 7 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Reducer 2 + Reduce Operator Tree: + Merge Join Operator + condition map: + Left Semi Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + Reducer 5 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Reducer 8 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Union 6 + Vertex: Union 6 + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +2008-04-08 +2008-04-09 +PREHOOK: query: EXPLAIN select ds from (select distinct(ds) as ds from srcpart union all select distinct(ds) as ds from srcpart) s where s.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select ds from (select distinct(ds) as ds from srcpart union all select distinct(ds) as ds from srcpart) s where s.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 11 <- Map 10 (SIMPLE_EDGE), Union 9 (CONTAINS) + Reducer 2 <- Map 1 (SIMPLE_EDGE), Union 3 (CONTAINS) + Reducer 4 <- Union 3 (SIMPLE_EDGE), Union 9 (SIMPLE_EDGE) + Reducer 6 <- Map 5 (SIMPLE_EDGE), Union 3 (CONTAINS) + Reducer 8 <- Map 7 (SIMPLE_EDGE), Union 9 (CONTAINS) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: ds (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 10 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Map 5 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: ds (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 7 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Reducer 11 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 5 + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reducer 4 + Reduce Operator Tree: + Merge Join Operator + condition map: + Left Semi Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + Reducer 6 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reducer 8 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 5 + Union 3 + Vertex: Union 3 + Union 9 + Vertex: Union 9 + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select ds from (select distinct(ds) as ds from srcpart union all select distinct(ds) as ds from srcpart) s where s.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select ds from (select distinct(ds) as ds from srcpart union all select distinct(ds) as ds from srcpart) s where s.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +2008-04-08 +2008-04-08 +2008-04-09 +2008-04-09 +PREHOOK: query: -- single column, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- single column, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: -- multiple sources, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- multiple sources, single key +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE), Map 4 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col1 + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col1 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 4 + Statistics: Num rows: 2420 Data size: 25709 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: hr + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +500 +PREHOOK: query: select count(*) from srcpart where hr = 11 and ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where hr = 11 and ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +500 +PREHOOK: query: -- multiple columns single source +EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- multiple columns single source +EXPLAIN select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string), _col1 (type: string) + 1 _col0 (type: string), _col2 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((((date = '2008-04-08') and (UDFToDouble(hour) = 11.0)) and ds is not null) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), _col2 (type: string) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), _col2 (type: string) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: _col2 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: hr + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date_hour on (srcpart.ds = srcpart_date_hour.ds and srcpart.hr = srcpart_date_hour.hr) where srcpart_date_hour.`date` = '2008-04-08' and srcpart_date_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_date_hour +#### A masked pattern was here #### +500 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' and hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' and hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +#### A masked pattern was here #### +500 +PREHOOK: query: -- empty set +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +PREHOOK: type: QUERY +POSTHOOK: query: -- empty set +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = 'I DONT EXIST') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- Disabled until TEZ-1486 is fixed +-- select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST'; + +-- expressions +EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- Disabled until TEZ-1486 is fixed +-- select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = 'I DONT EXIST'; + +-- expressions +EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 UDFToDouble(_col0) (type: double) + 1 UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + input vertices: + 1 Map 3 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and UDFToDouble(UDFToInteger((hr / 2.0))) is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + sort order: + + Map-reduce partition columns: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: UDFToDouble(UDFToInteger((_col0 / 2.0))) (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: double) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: UDFToDouble(hr) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr = cast(srcpart_double_hour.hr/2 as int)) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (UDFToDouble(hr) * 2.0) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 (UDFToDouble(_col0) * 2.0) (type: double) + 1 _col0 (type: double) + input vertices: + 1 Map 3 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_double_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and hr is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: hr (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: double) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: (UDFToDouble(hr) * 2.0) + Statistics: Num rows: 1 Data size: 94 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_double_hour on (srcpart.hr*2 = srcpart_double_hour.hr) where srcpart_double_hour.hour = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Input: default@srcpart_double_hour +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +1000 +Warning: Map Join MAPJOIN[21][bigTable=?] in task 'Reducer 3' is a cross product +PREHOOK: query: -- parent is reduce tasks +EXPLAIN select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- parent is reduce tasks +EXPLAIN select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Reducer 3 <- Map 1 (BROADCAST_EDGE), Map 2 (SIMPLE_EDGE) + Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds = '2008-04-08') (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map 2 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds = '2008-04-08') (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: '2008-04-08' (type: string) + outputColumnNames: ds + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: ds (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 + 1 + input vertices: + 0 Map 1 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +Warning: Map Join MAPJOIN[21][bigTable=?] in task 'Reducer 3' is a cross product +PREHOOK: query: select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join (select ds as ds, ds as `date` from srcpart group by ds) s on (srcpart.ds = s.ds) where s.`date` = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where ds = '2008-04-08' +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: -- left join +EXPLAIN select count(*) from srcpart left join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- left join +EXPLAIN select count(*) from srcpart left join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: EXPLAIN select count(*) from srcpart_date left join srcpart on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart_date left join srcpart on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Left Outer Join0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Execution mode: vectorized + Map 3 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[5]) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- full outer +EXPLAIN select count(*) from srcpart full outer join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +PREHOOK: type: QUERY +POSTHOOK: query: -- full outer +EXPLAIN select count(*) from srcpart full outer join srcpart_date on (srcpart.ds = srcpart_date.ds) where srcpart_date.`date` = '2008-04-08' +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 2 <- Map 1 (BROADCAST_EDGE) + Reducer 3 <- Map 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[6]) (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map 2 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (date = '2008-04-08') (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Right Outer Join0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 0 Map 1 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Execution mode: vectorized + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- with static pruning +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +PREHOOK: type: QUERY +POSTHOOK: query: -- with static pruning +EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE), Map 4 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: (ds) IN (RS[14]) (type: boolean) + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1000 Data size: 10624 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 1 Map 3 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 '11' (type: string) + 1 '11' (type: string) + input vertices: + 1 Map 4 + Statistics: Num rows: 1210 Data size: 12854 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Execution mode: vectorized + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: ((UDFToDouble(hour) = 11.0) and (UDFToDouble(hr) = 11.0)) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hour) = 11.0) and (UDFToDouble(hr) = 11.0)) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: '11' (type: string) + sort order: + + Map-reduce partition columns: '11' (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart_date +PREHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart_hour.hour = 11 and srcpart.hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Input: default@srcpart_hour +#### A masked pattern was here #### +500 +PREHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13 +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 2 <- Map 1 (BROADCAST_EDGE), Map 4 (BROADCAST_EDGE) + Reducer 3 <- Map 2 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ((UDFToDouble(hr) = 13.0) and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Filter Operator + predicate: ((UDFToDouble(hr) = 13.0) and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + Execution mode: vectorized + Map 2 + Map Operator Tree: + TableScan + alias: srcpart_date + filterExpr: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 2 Data size: 376 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: ((date = '2008-04-08') and ds is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + input vertices: + 0 Map 1 + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + HybridGraceHashJoin: true + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 '13' (type: string) + 1 '13' (type: string) + input vertices: + 1 Map 4 + Statistics: Num rows: 1 Data size: 0 Basic stats: PARTIAL Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Execution mode: vectorized + Map 4 + Map Operator Tree: + TableScan + alias: srcpart_hour + filterExpr: (UDFToDouble(hr) = 13.0) (type: boolean) + Statistics: Num rows: 2 Data size: 344 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (UDFToDouble(hr) = 13.0) (type: boolean) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Select Operator + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: '13' (type: string) + sort order: + + Map-reduce partition columns: '13' (type: string) + Statistics: Num rows: 1 Data size: 172 Basic stats: COMPLETE Column stats: NONE + Execution mode: vectorized + Reducer 3 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: -- Disabled until TEZ-1486 is fixed +-- select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +-- where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13; + +-- union + subquery +EXPLAIN select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +POSTHOOK: query: -- Disabled until TEZ-1486 is fixed +-- select count(*) from srcpart join srcpart_date on (srcpart.ds = srcpart_date.ds) join srcpart_hour on (srcpart.hr = srcpart_hour.hr) +-- where srcpart_date.`date` = '2008-04-08' and srcpart.hr = 13; + +-- union + subquery +EXPLAIN select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Union 5 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) + Reducer 4 <- Map 3 (SIMPLE_EDGE), Union 5 (CONTAINS) + Reducer 7 <- Map 6 (SIMPLE_EDGE), Union 5 (CONTAINS) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart + filterExpr: ds is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Left Semi Join 0 to 1 + keys: + 0 _col0 (type: string) + 1 _col0 (type: string) + outputColumnNames: _col0 + input vertices: + 1 Union 5 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2200 Data size: 23372 Basic stats: COMPLETE Column stats: NONE + Map 3 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Map 6 + Map Operator Tree: + TableScan + alias: srcpart + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string) + outputColumnNames: ds + Statistics: Num rows: 2000 Data size: 21248 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(ds) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: string) + Reducer 2 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1100 Data size: 11686 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + Reducer 4 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Reducer 7 + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: _col0 is not null (type: boolean) + Statistics: Num rows: 1 Data size: 84 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart + Partition key expr: ds + Statistics: Num rows: 2 Data size: 168 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Union 5 + Vertex: Union 5 + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select distinct(ds) from srcpart where srcpart.ds in (select max(srcpart.ds) from srcpart union all select min(srcpart.ds) from srcpart) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +2008-04-08 +2008-04-09 +PREHOOK: query: -- different file format +create table srcpart_orc (key int, value string) partitioned by (ds string, hr int) stored as orc +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@srcpart_orc +POSTHOOK: query: -- different file format +create table srcpart_orc (key int, value string) partitioned by (ds string, hr int) stored as orc +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@srcpart_orc +PREHOOK: query: insert into table srcpart_orc partition (ds, hr) select key, value, ds, hr from srcpart +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +PREHOOK: Output: default@srcpart_orc +POSTHOOK: query: insert into table srcpart_orc partition (ds, hr) select key, value, ds, hr from srcpart +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=12 +POSTHOOK: Output: default@srcpart_orc@ds=2008-04-08/hr=11 +POSTHOOK: Output: default@srcpart_orc@ds=2008-04-08/hr=12 +POSTHOOK: Output: default@srcpart_orc@ds=2008-04-09/hr=11 +POSTHOOK: Output: default@srcpart_orc@ds=2008-04-09/hr=12 +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-08,hr=11).key EXPRESSION [(srcpart)srcpart.FieldSchema(name:key, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-08,hr=11).value SIMPLE [(srcpart)srcpart.FieldSchema(name:value, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-08,hr=12).key EXPRESSION [(srcpart)srcpart.FieldSchema(name:key, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-08,hr=12).value SIMPLE [(srcpart)srcpart.FieldSchema(name:value, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-09,hr=11).key EXPRESSION [(srcpart)srcpart.FieldSchema(name:key, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-09,hr=11).value SIMPLE [(srcpart)srcpart.FieldSchema(name:value, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-09,hr=12).key EXPRESSION [(srcpart)srcpart.FieldSchema(name:key, type:string, comment:default), ] +POSTHOOK: Lineage: srcpart_orc PARTITION(ds=2008-04-09,hr=12).value SIMPLE [(srcpart)srcpart.FieldSchema(name:value, type:string, comment:default), ] +PREHOOK: query: EXPLAIN select count(*) from srcpart_orc join srcpart_date_hour on (srcpart_orc.ds = srcpart_date_hour.ds and srcpart_orc.hr = srcpart_date_hour.hr) where srcpart_date_hour.hour = 11 and (srcpart_date_hour.`date` = '2008-04-08' or srcpart_date_hour.`date` = '2008-04-09') +PREHOOK: type: QUERY +POSTHOOK: query: EXPLAIN select count(*) from srcpart_orc join srcpart_date_hour on (srcpart_orc.ds = srcpart_date_hour.ds and srcpart_orc.hr = srcpart_date_hour.hr) where srcpart_date_hour.hour = 11 and (srcpart_date_hour.`date` = '2008-04-08' or srcpart_date_hour.`date` = '2008-04-09') +POSTHOOK: type: QUERY +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Tez + Edges: + Map 1 <- Map 3 (BROADCAST_EDGE) + Reducer 2 <- Map 1 (SIMPLE_EDGE) +#### A masked pattern was here #### + Vertices: + Map 1 + Map Operator Tree: + TableScan + alias: srcpart_orc + filterExpr: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 2000 Data size: 188000 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: UDFToDouble(hr) is not null (type: boolean) + Statistics: Num rows: 1000 Data size: 94000 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: int) + outputColumnNames: _col0, _col1 + Statistics: Num rows: 1000 Data size: 94000 Basic stats: COMPLETE Column stats: NONE + Map Join Operator + condition map: + Inner Join 0 to 1 + keys: + 0 _col0 (type: string), UDFToDouble(_col1) (type: double) + 1 _col0 (type: string), UDFToDouble(_col2) (type: double) + input vertices: + 1 Map 3 + Statistics: Num rows: 1100 Data size: 103400 Basic stats: COMPLETE Column stats: NONE + HybridGraceHashJoin: true + Group By Operator + aggregations: count() + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + sort order: + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + value expressions: _col0 (type: bigint) + Map 3 + Map Operator Tree: + TableScan + alias: srcpart_date_hour + filterExpr: (((((date = '2008-04-08') or (date = '2008-04-09')) and (UDFToDouble(hour) = 11.0)) and ds is not null) and UDFToDouble(hr) is not null) (type: boolean) + Statistics: Num rows: 4 Data size: 1440 Basic stats: COMPLETE Column stats: NONE + Filter Operator + predicate: (((((date = '2008-04-08') or (date = '2008-04-09')) and (UDFToDouble(hour) = 11.0)) and ds is not null) and UDFToDouble(hr) is not null) (type: boolean) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: ds (type: string), hr (type: string) + outputColumnNames: _col0, _col2 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string), UDFToDouble(_col2) (type: double) + sort order: ++ + Map-reduce partition columns: _col0 (type: string), UDFToDouble(_col2) (type: double) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col0 (type: string) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: string) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart_orc + Partition key expr: ds + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: ds + Target Vertex: Map 1 + Select Operator + expressions: UDFToDouble(_col2) (type: double) + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Group By Operator + keys: _col0 (type: double) + mode: hash + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Dynamic Partitioning Event Operator + Target Input: srcpart_orc + Partition key expr: UDFToDouble(hr) + Statistics: Num rows: 1 Data size: 360 Basic stats: COMPLETE Column stats: NONE + Target column: hr + Target Vertex: Map 1 + Reducer 2 + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + mode: mergepartial + outputColumnNames: _col0 + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select count(*) from srcpart_orc join srcpart_date_hour on (srcpart_orc.ds = srcpart_date_hour.ds and srcpart_orc.hr = srcpart_date_hour.hr) where srcpart_date_hour.hour = 11 and (srcpart_date_hour.`date` = '2008-04-08' or srcpart_date_hour.`date` = '2008-04-09') +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart_date_hour +PREHOOK: Input: default@srcpart_orc +PREHOOK: Input: default@srcpart_orc@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart_orc@ds=2008-04-08/hr=12 +PREHOOK: Input: default@srcpart_orc@ds=2008-04-09/hr=11 +PREHOOK: Input: default@srcpart_orc@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart_orc join srcpart_date_hour on (srcpart_orc.ds = srcpart_date_hour.ds and srcpart_orc.hr = srcpart_date_hour.hr) where srcpart_date_hour.hour = 11 and (srcpart_date_hour.`date` = '2008-04-08' or srcpart_date_hour.`date` = '2008-04-09') +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart_date_hour +POSTHOOK: Input: default@srcpart_orc +POSTHOOK: Input: default@srcpart_orc@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart_orc@ds=2008-04-08/hr=12 +POSTHOOK: Input: default@srcpart_orc@ds=2008-04-09/hr=11 +POSTHOOK: Input: default@srcpart_orc@ds=2008-04-09/hr=12 +#### A masked pattern was here #### +1000 +PREHOOK: query: select count(*) from srcpart where (ds = '2008-04-08' or ds = '2008-04-09') and hr = 11 +PREHOOK: type: QUERY +PREHOOK: Input: default@srcpart +PREHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +PREHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +POSTHOOK: query: select count(*) from srcpart where (ds = '2008-04-08' or ds = '2008-04-09') and hr = 11 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@srcpart +POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=11 +POSTHOOK: Input: default@srcpart@ds=2008-04-09/hr=11 +#### A masked pattern was here #### +1000 +PREHOOK: query: drop table srcpart_orc +PREHOOK: type: DROPTABLE +PREHOOK: Input: default@srcpart_orc +PREHOOK: Output: default@srcpart_orc +POSTHOOK: query: drop table srcpart_orc +POSTHOOK: type: DROPTABLE +POSTHOOK: Input: default@srcpart_orc +POSTHOOK: Output: default@srcpart_orc +PREHOOK: query: drop table srcpart_date +PREHOOK: type: DROPTABLE +PREHOOK: Input: default@srcpart_date +PREHOOK: Output: default@srcpart_date +POSTHOOK: query: drop table srcpart_date +POSTHOOK: type: DROPTABLE +POSTHOOK: Input: default@srcpart_date +POSTHOOK: Output: default@srcpart_date +PREHOOK: query: drop table srcpart_hour +PREHOOK: type: DROPTABLE +PREHOOK: Input: default@srcpart_hour +PREHOOK: Output: default@srcpart_hour +POSTHOOK: query: drop table srcpart_hour +POSTHOOK: type: DROPTABLE +POSTHOOK: Input: default@srcpart_hour +POSTHOOK: Output: default@srcpart_hour +PREHOOK: query: drop table srcpart_date_hour +PREHOOK: type: DROPTABLE +PREHOOK: Input: default@srcpart_date_hour +PREHOOK: Output: default@srcpart_date_hour +POSTHOOK: query: drop table srcpart_date_hour +POSTHOOK: type: DROPTABLE +POSTHOOK: Input: default@srcpart_date_hour +POSTHOOK: Output: default@srcpart_date_hour +PREHOOK: query: drop table srcpart_double_hour +PREHOOK: type: DROPTABLE +PREHOOK: Input: default@srcpart_double_hour +PREHOOK: Output: default@srcpart_double_hour +POSTHOOK: query: drop table srcpart_double_hour +POSTHOOK: type: DROPTABLE +POSTHOOK: Input: default@srcpart_double_hour +POSTHOOK: Output: default@srcpart_double_hour diff --git ql/src/test/results/clientpositive/tez/vectorized_nested_mapjoin.q.out ql/src/test/results/clientpositive/tez/vectorized_nested_mapjoin.q.out index 3f7dd12..46dae3a 100644 --- ql/src/test/results/clientpositive/tez/vectorized_nested_mapjoin.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_nested_mapjoin.q.out @@ -104,7 +104,6 @@ STAGE PLANS: Statistics: Num rows: 6144 Data size: 1320982 Basic stats: COMPLETE Column stats: NONE Execution mode: vectorized Reducer 3 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) diff --git ql/src/test/results/clientpositive/tez/vectorized_parquet_types.q.out ql/src/test/results/clientpositive/tez/vectorized_parquet_types.q.out index 0cb2270..566f4f1 100644 --- ql/src/test/results/clientpositive/tez/vectorized_parquet_types.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_parquet_types.q.out @@ -249,12 +249,12 @@ Stage-0 Fetch Operator limit:-1 Stage-1 - Reducer 3 vectorized - File Output Operator [FS_10] + Reducer 3 + File Output Operator [FS_8] compressed:false Statistics:Num rows: 11 Data size: 121 Basic stats: COMPLETE Column stats: NONE table:{"input format:":"org.apache.hadoop.mapred.TextInputFormat","output format:":"org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","serde:":"org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"} - Select Operator [OP_9] + Select Operator [SEL_7] | outputColumnNames:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"] | Statistics:Num rows: 11 Data size: 121 Basic stats: COMPLETE Column stats: NONE |<-Reducer 2 [SIMPLE_EDGE] diff --git ql/src/test/results/clientpositive/tez/vectorized_ptf.q.out ql/src/test/results/clientpositive/tez/vectorized_ptf.q.out index 687085d..b828798 100644 --- ql/src/test/results/clientpositive/tez/vectorized_ptf.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_ptf.q.out @@ -5145,7 +5145,6 @@ STAGE PLANS: tag: -1 auto parallelism: true Reducer 3 - Execution mode: vectorized Needs Tagging: false Reduce Operator Tree: Group By Operator diff --git ql/src/test/results/clientpositive/tez/vectorized_shufflejoin.q.out ql/src/test/results/clientpositive/tez/vectorized_shufflejoin.q.out index 1735cb2..70f9e38 100644 --- ql/src/test/results/clientpositive/tez/vectorized_shufflejoin.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_shufflejoin.q.out @@ -95,7 +95,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 24 Basic stats: COMPLETE Column stats: NONE value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: double) Reducer 4 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), VALUE._col0 (type: int), VALUE._col1 (type: int), VALUE._col2 (type: double) diff --git ql/src/test/results/clientpositive/tez/vectorized_timestamp_funcs.q.out ql/src/test/results/clientpositive/tez/vectorized_timestamp_funcs.q.out index 9626cde..cc6f07e 100644 --- ql/src/test/results/clientpositive/tez/vectorized_timestamp_funcs.q.out +++ ql/src/test/results/clientpositive/tez/vectorized_timestamp_funcs.q.out @@ -126,7 +126,6 @@ STAGE PLANS: value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: int), _col8 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), VALUE._col0 (type: int), VALUE._col1 (type: int), VALUE._col2 (type: int), VALUE._col3 (type: int), VALUE._col4 (type: int), VALUE._col5 (type: int), VALUE._col6 (type: int), VALUE._col7 (type: int) @@ -269,7 +268,6 @@ STAGE PLANS: value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: int), _col8 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), VALUE._col0 (type: int), VALUE._col1 (type: int), VALUE._col2 (type: int), VALUE._col3 (type: int), VALUE._col4 (type: int), VALUE._col5 (type: int), VALUE._col6 (type: int), VALUE._col7 (type: int) @@ -412,7 +410,6 @@ STAGE PLANS: value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: boolean), VALUE._col0 (type: boolean), VALUE._col1 (type: boolean), VALUE._col2 (type: boolean), VALUE._col3 (type: boolean), VALUE._col4 (type: boolean), VALUE._col5 (type: boolean), VALUE._col6 (type: boolean), VALUE._col7 (type: boolean) @@ -559,7 +556,6 @@ STAGE PLANS: value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: int), _col7 (type: int), _col8 (type: int) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Select Operator expressions: KEY.reducesinkkey0 (type: bigint), VALUE._col0 (type: int), VALUE._col1 (type: int), VALUE._col2 (type: int), VALUE._col3 (type: int), VALUE._col4 (type: int), VALUE._col5 (type: int), VALUE._col6 (type: int), VALUE._col7 (type: int) @@ -657,7 +653,6 @@ STAGE PLANS: value expressions: _col0 (type: timestamp), _col1 (type: timestamp), _col2 (type: bigint), _col3 (type: bigint) Execution mode: vectorized Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: min(VALUE._col0), max(VALUE._col1), count(VALUE._col2), count(VALUE._col3) @@ -737,7 +732,6 @@ STAGE PLANS: Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE value expressions: _col0 (type: double) Reducer 2 - Execution mode: vectorized Reduce Operator Tree: Group By Operator aggregations: sum(VALUE._col0) diff --git ql/src/test/results/clientpositive/vector_groupby1.q.out ql/src/test/results/clientpositive/vector_groupby1.q.out new file mode 100644 index 0000000..0e1e44a --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby1.q.out @@ -0,0 +1,828 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single Long Aggregations +explain +select b, count(*) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single Long Aggregations +explain +select b, count(*) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint) + outputColumnNames: b + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, count(*) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(*) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 1 +-7049618574399692800 1 +-7496839341561954304 1 +-7911421221625077760 1 +-8831091081349758976 1 +-9014145341570203648 1 +1075 1 +1719 1 +1774 1 +2241 1 +2607 1 +302 1 +3118 1 +3764 1 +504 1 +661 1 +7250237407877382144 1 +7432428551399669760 1 +7659279803863146496 1 +7792036342592348160 1 +78 1 +8100036735858401280 1 +8283099811330506752 1 +8895174927321243648 1 +9182828596851990528 1 +PREHOOK: query: select b, min(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, min(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, sum(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, sum(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 -6935038507792801792 +-7049618574399692800 -7049618574399692800 +-7496839341561954304 -7496839341561954304 +-7911421221625077760 -7911421221625077760 +-8831091081349758976 -8831091081349758976 +-9014145341570203648 -9014145341570203648 +1075 1075 +1719 1719 +1774 1774 +2241 2241 +2607 2607 +302 302 +3118 3118 +3764 3764 +504 504 +661 661 +7250237407877382144 7250237407877382144 +7432428551399669760 7432428551399669760 +7659279803863146496 7659279803863146496 +7792036342592348160 7792036342592348160 +78 78 +8100036735858401280 8100036735858401280 +8283099811330506752 8283099811330506752 +8895174927321243648 8895174927321243648 +9182828596851990528 9182828596851990528 +PREHOOK: query: select b, count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 +-6935038507792801792 1 +-7049618574399692800 1 +-7496839341561954304 1 +-7911421221625077760 1 +-8831091081349758976 1 +-9014145341570203648 1 +1075 1 +1719 1 +1774 1 +2241 1 +2607 1 +302 1 +3118 1 +3764 1 +504 1 +661 1 +7250237407877382144 1 +7432428551399669760 1 +7659279803863146496 1 +7792036342592348160 1 +78 1 +8100036735858401280 1 +8283099811330506752 1 +8895174927321243648 1 +9182828596851990528 1 +PREHOOK: query: select i, count(*) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, count(*) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 1 +-146961490 1 +-1730740504 1 +-1832606512 1 +-1974777102 1 +-2027812975 1 +-407089271 1 +-522450861 1 +-538812082 1 +-772236518 1 +-805288503 1 +-978892011 1 +1008698636 1 +1541249928 1 +174310705 1 +273256071 1 +476704350 1 +737149747 1 +810157660 1 +851975276 1 +868714547 1 +95356298 1 +NULL 3 +PREHOOK: query: select i, min(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, min(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, max(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, max(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, sum(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, sum(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 -1012329052 +-146961490 -146961490 +-1730740504 -1730740504 +-1832606512 -1832606512 +-1974777102 -1974777102 +-2027812975 -2027812975 +-407089271 -407089271 +-522450861 -522450861 +-538812082 -538812082 +-772236518 -772236518 +-805288503 -805288503 +-978892011 -978892011 +1008698636 1008698636 +1541249928 1541249928 +174310705 174310705 +273256071 273256071 +476704350 476704350 +737149747 737149747 +810157660 810157660 +851975276 851975276 +868714547 868714547 +95356298 95356298 +NULL NULL +PREHOOK: query: select i, count(i) from vectortab2korc group by i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i, count(i) from vectortab2korc group by i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i c1 +-1012329052 1 +-146961490 1 +-1730740504 1 +-1832606512 1 +-1974777102 1 +-2027812975 1 +-407089271 1 +-522450861 1 +-538812082 1 +-772236518 1 +-805288503 1 +-978892011 1 +1008698636 1 +1541249928 1 +174310705 1 +273256071 1 +476704350 1 +737149747 1 +810157660 1 +851975276 1 +868714547 1 +95356298 1 +NULL 0 +PREHOOK: query: select si, count(*) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, count(*) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 1 +-1928 1 +-21772 1 +-28501 1 +-30304 1 +-31395 1 +-9171 1 +-9609 1 +133 1 +14773 1 +16195 1 +21091 1 +21849 1 +23441 1 +26557 1 +2671 1 +27922 1 +30921 1 +725 1 +7353 1 +7401 1 +8918 1 +NULL 3 +PREHOOK: query: select si, min(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, min(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, max(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, max(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, sum(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, sum(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 -10317 +-1928 -1928 +-21772 -21772 +-28501 -28501 +-30304 -30304 +-31395 -31395 +-9171 -9171 +-9609 -9609 +133 133 +14773 14773 +16195 16195 +21091 21091 +21849 21849 +23441 23441 +26557 26557 +2671 2671 +27922 27922 +30921 30921 +725 725 +7353 7353 +7401 7401 +8918 8918 +NULL NULL +PREHOOK: query: select si, count(si) from vectortab2korc group by si +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si, count(si) from vectortab2korc group by si +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si c1 +-10317 1 +-1928 1 +-21772 1 +-28501 1 +-30304 1 +-31395 1 +-9171 1 +-9609 1 +133 1 +14773 1 +16195 1 +21091 1 +21849 1 +23441 1 +26557 1 +2671 1 +27922 1 +30921 1 +725 1 +7353 1 +7401 1 +8918 1 +NULL 0 +PREHOOK: query: select t, count(*) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, count(*) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 1 +-19 1 +-29 1 +-35 1 +-4 1 +-43 1 +-46 1 +-57 1 +-58 1 +-87 1 +-92 1 +111 1 +23 1 +31 1 +36 1 +40 1 +47 1 +48 1 +52 1 +54 1 +55 1 +7 1 +73 1 +95 1 +NULL 1 +PREHOOK: query: select t, min(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, min(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, max(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, max(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, sum(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, sum(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 -127 +-19 -19 +-29 -29 +-35 -35 +-4 -4 +-43 -43 +-46 -46 +-57 -57 +-58 -58 +-87 -87 +-92 -92 +111 111 +23 23 +31 31 +36 36 +40 40 +47 47 +48 48 +52 52 +54 54 +55 55 +7 7 +73 73 +95 95 +NULL NULL +PREHOOK: query: select t, count(t) from vectortab2korc group by t +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t, count(t) from vectortab2korc group by t +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t c1 +-127 1 +-19 1 +-29 1 +-35 1 +-4 1 +-43 1 +-46 1 +-57 1 +-58 1 +-87 1 +-92 1 +111 1 +23 1 +31 1 +36 1 +40 1 +47 1 +48 1 +52 1 +54 1 +55 1 +7 1 +73 1 +95 1 +NULL 0 diff --git ql/src/test/results/clientpositive/vector_groupby10.q.out ql/src/test/results/clientpositive/vector_groupby10.q.out new file mode 100644 index 0000000..5396854 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby10.q.out @@ -0,0 +1,312 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Long Aggregations with String key +explain +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Long Aggregations with String key +explain +select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: s (type: string), b (type: bigint) + outputColumnNames: s, b + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(b), max(b), sum(b), count(b) + keys: s (type: string) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: bigint), _col5 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -9080568167841226752 9191943992860327936 7768516223596009040 87 +american history 80 -9175038118837149696 9119046173224370176 -1784196330193843507 78 +biology 91 -9148197394287779840 9116137265342169088 -7961209961346096620 86 +chemistry 79 -9178166810751909888 9091085792947666944 -6666869272136986436 75 +debate 71 -8856821118526734336 9078604269481148416 -7392142879918672005 70 +education 83 -8930307926221807616 9148071980848742400 -7854354868350908468 80 +forestry 76 -9175279464813223936 9194388393453060096 -6596128059684168313 75 +geology 76 -9101953184875757568 9020143715350814720 1913939625854701960 73 +history 74 -9145593811310010368 8987827141270880256 1082457997392013425 71 +industrial engineering 63 -9215144824304721920 9185458640237641728 651091220914778141 61 +joggying 73 -8877053610728161280 8936639033158410240 650723507550745725 70 +kindergarten 61 -9013952631912325120 9207927479837319168 4060088858295423582 58 +linguistics 69 -9203804401302323200 9188173682239275008 -9128943995720928788 63 +mathematics 75 -9075302542655684608 9001907486943993856 -4843070688786362221 73 +nap time 56 -9218875542187065344 9136234417125007360 6818257282609481999 52 +opthamology 75 -9117959922369060864 9131533983989358592 1367237735070318883 71 +philosophy 66 -8710298418608619520 9199741683232399360 4551016788373995594 66 +quiet hour 67 -9008631121684832256 9182828596851990528 5146025034009544543 63 +religion 76 -9213132862973829120 9211455920344088576 2252876202322280954 74 +study skills 77 -9187662685618348032 9132009829414584320 1705035481933188172 75 +topology 69 -9189155542884474880 8900180888218329088 -2309069353931158255 67 +undecided 67 -9157613004431998976 9190466190353661952 4204051913884115722 64 +values clariffication 84 -9210275791460499456 9107991000536498176 -5026363385883231434 77 +wind surfing 90 -9149719074367946752 9169248521377374208 -4552714315226545517 88 +xylophone band 71 -9051477157204770816 9209153648361848832 -1282123898947113698 70 +yard duty 67 -9126793997498957824 9139805788041134080 -5411899933644306220 62 +zync studies 70 -9219066990552760320 9104574294205636608 8089923339479872437 68 +PREHOOK: query: select s, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(i), max(i), sum(i), count(i) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -2136052026 2144365072 1533003218 89 +american history 80 -2144138362 2051470532 -851194217 75 +biology 91 -2086352100 2045579147 -4969295497 90 +chemistry 79 -2133145181 2111462911 -941014464 76 +debate 71 -2071851852 2044130430 1996299495 68 +education 83 -2027812975 2140632003 13547286481 79 +forestry 76 -2041825946 2097519027 19824997175 72 +geology 76 -2042647152 2068538934 -1405170115 75 +history 74 -2076460151 1844415080 743244477 71 +industrial engineering 63 -2137168636 2114363167 1229906572 58 +joggying 73 -2119724898 2133950868 -1096929419 67 +kindergarten 61 -1988508336 2089198703 -2313595554 58 +linguistics 69 -2096425960 2081152819 -3983960709 65 +mathematics 75 -2117280385 2064448036 -5282605659 69 +nap time 56 -2111312205 2146312499 8624321050 54 +opthamology 75 -2081501748 2069258195 -5533266628 73 +philosophy 66 -2077771325 2124297747 -12421316315 65 +quiet hour 67 -2069439395 2127682701 18873259951 64 +religion 76 -2146432765 2125311222 6681439722 70 +study skills 77 -1955647385 2032271149 10765031439 75 +topology 69 -2147071655 2075919195 2829175314 65 +undecided 67 -2124994385 2102440065 20707906833 65 +values clariffication 84 -2098078720 2142592987 -2202587383 80 +wind surfing 90 -2037628236 1949494660 -8566294168 90 +xylophone band 71 -2138343289 2052773366 -20411810546 70 +yard duty 67 -1983567458 2133492883 568080693 60 +zync studies 70 -2144241640 2144454927 3509622193 65 +PREHOOK: query: select s, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(si), max(si), sum(si), count(si) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -32491 31923 -441191 89 +american history 80 -32296 32734 99245 75 +biology 91 -32364 32070 113587 86 +chemistry 79 -32755 32019 96819 72 +debate 71 -31596 31390 13677 68 +education 83 -32022 32581 319955 77 +forestry 76 -32266 32589 -73468 68 +geology 76 -32533 30824 -215963 72 +history 74 -31335 31374 -30938 68 +industrial engineering 63 -31395 31706 77646 60 +joggying 73 -31164 32584 77893 70 +kindergarten 61 -31711 32231 -196667 55 +linguistics 69 -31663 32309 50926 65 +mathematics 75 -31998 32210 -137942 69 +nap time 56 -32498 30420 -154592 53 +opthamology 75 -32688 28558 -250546 68 +philosophy 66 -32403 31427 32410 64 +quiet hour 67 -32383 32640 77895 65 +religion 76 -32755 31721 -189965 73 +study skills 77 -32462 32611 234580 73 +topology 69 -30677 32671 156119 65 +undecided 67 -32541 30861 104935 63 +values clariffication 84 -31130 32420 -129978 82 +wind surfing 90 -31182 32547 334213 82 +xylophone band 71 -29907 32212 -47875 67 +yard duty 67 -32240 32563 -217944 63 +zync studies 70 -32119 32669 27391 69 +PREHOOK: query: select s, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*), min(t), max(t), sum(t), count(t) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 c2 c3 c4 c5 + 94 -116 123 610 91 +american history 80 -127 123 -806 76 +biology 91 -127 124 -545 88 +chemistry 79 -127 126 -668 75 +debate 71 -127 124 -264 67 +education 83 -127 125 -35 78 +forestry 76 -126 120 623 72 +geology 76 -124 127 -220 74 +history 74 -125 127 297 69 +industrial engineering 63 -124 126 261 62 +joggying 73 -125 125 774 72 +kindergarten 61 -126 127 969 58 +linguistics 69 -127 126 268 67 +mathematics 75 -127 114 -352 72 +nap time 56 -122 118 150 55 +opthamology 75 -122 127 309 73 +philosophy 66 -125 123 -447 61 +quiet hour 67 -127 123 -349 61 +religion 76 -125 124 745 73 +study skills 77 -127 123 704 73 +topology 69 -122 127 418 65 +undecided 67 -120 124 -816 63 +values clariffication 84 -123 127 -291 83 +wind surfing 90 -124 121 667 82 +xylophone band 71 -115 127 505 67 +yard duty 67 -127 110 -975 63 +zync studies 70 -127 120 -956 64 diff --git ql/src/test/results/clientpositive/vector_groupby11.q.out ql/src/test/results/clientpositive/vector_groupby11.q.out new file mode 100644 index 0000000..e270401 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby11.q.out @@ -0,0 +1,854 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Multiple Long Key Aggregations (not including min, max, sum) +-- +explain +select b,i,count(*) from vectortab2korc group by b,i +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Multiple Long Key Aggregations (not including min, max, sum) +-- +explain +select b,i,count(*) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: i (type: int), b (type: bigint) + outputColumnNames: i, b + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: i (type: int), b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: int), _col1 (type: bigint) + sort order: ++ + Map-reduce partition columns: _col0 (type: int), _col1 (type: bigint) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col2 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: int), KEY._col1 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: _col1 (type: bigint), _col0 (type: int), _col2 (type: bigint) + outputColumnNames: _col0, _col1, _col2 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b,i,count(*) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,count(*) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 1 +-7049618574399692800 -978892011 1 +-7496839341561954304 868714547 1 +-7911421221625077760 476704350 1 +-8831091081349758976 -1832606512 1 +-9014145341570203648 273256071 1 +1075 NULL 1 +1719 1008698636 1 +1774 -1974777102 1 +2241 810157660 1 +2607 NULL 1 +302 -1730740504 1 +3118 NULL 1 +3764 -2027812975 1 +504 851975276 1 +661 -407089271 1 +7250237407877382144 -772236518 1 +7432428551399669760 -805288503 1 +7659279803863146496 1541249928 1 +7792036342592348160 -538812082 1 +78 95356298 1 +8100036735858401280 -146961490 1 +8283099811330506752 737149747 1 +8895174927321243648 -522450861 1 +9182828596851990528 -1012329052 1 +PREHOOK: query: select b,i,min(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,min(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,max(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,max(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,sum(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,sum(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 -6935038507792801792 +-7049618574399692800 -978892011 -7049618574399692800 +-7496839341561954304 868714547 -7496839341561954304 +-7911421221625077760 476704350 -7911421221625077760 +-8831091081349758976 -1832606512 -8831091081349758976 +-9014145341570203648 273256071 -9014145341570203648 +1075 NULL 1075 +1719 1008698636 1719 +1774 -1974777102 1774 +2241 810157660 2241 +2607 NULL 2607 +302 -1730740504 302 +3118 NULL 3118 +3764 -2027812975 3764 +504 851975276 504 +661 -407089271 661 +7250237407877382144 -772236518 7250237407877382144 +7432428551399669760 -805288503 7432428551399669760 +7659279803863146496 1541249928 7659279803863146496 +7792036342592348160 -538812082 7792036342592348160 +78 95356298 78 +8100036735858401280 -146961490 8100036735858401280 +8283099811330506752 737149747 8283099811330506752 +8895174927321243648 -522450861 8895174927321243648 +9182828596851990528 -1012329052 9182828596851990528 +PREHOOK: query: select b,i,count(b) from vectortab2korc group by b,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b,i,count(b) from vectortab2korc group by b,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b i c2 +-6935038507792801792 174310705 1 +-7049618574399692800 -978892011 1 +-7496839341561954304 868714547 1 +-7911421221625077760 476704350 1 +-8831091081349758976 -1832606512 1 +-9014145341570203648 273256071 1 +1075 NULL 1 +1719 1008698636 1 +1774 -1974777102 1 +2241 810157660 1 +2607 NULL 1 +302 -1730740504 1 +3118 NULL 1 +3764 -2027812975 1 +504 851975276 1 +661 -407089271 1 +7250237407877382144 -772236518 1 +7432428551399669760 -805288503 1 +7659279803863146496 1541249928 1 +7792036342592348160 -538812082 1 +78 95356298 1 +8100036735858401280 -146961490 1 +8283099811330506752 737149747 1 +8895174927321243648 -522450861 1 +9182828596851990528 -1012329052 1 +PREHOOK: query: select i,b,count(*) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,count(*) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 1 +-146961490 8100036735858401280 1 +-1730740504 302 1 +-1832606512 -8831091081349758976 1 +-1974777102 1774 1 +-2027812975 3764 1 +-407089271 661 1 +-522450861 8895174927321243648 1 +-538812082 7792036342592348160 1 +-772236518 7250237407877382144 1 +-805288503 7432428551399669760 1 +-978892011 -7049618574399692800 1 +1008698636 1719 1 +1541249928 7659279803863146496 1 +174310705 -6935038507792801792 1 +273256071 -9014145341570203648 1 +476704350 -7911421221625077760 1 +737149747 8283099811330506752 1 +810157660 2241 1 +851975276 504 1 +868714547 -7496839341561954304 1 +95356298 78 1 +NULL 1075 1 +NULL 2607 1 +NULL 3118 1 +PREHOOK: query: select i,b,min(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,min(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,max(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,max(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,sum(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,sum(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 -1012329052 +-146961490 8100036735858401280 -146961490 +-1730740504 302 -1730740504 +-1832606512 -8831091081349758976 -1832606512 +-1974777102 1774 -1974777102 +-2027812975 3764 -2027812975 +-407089271 661 -407089271 +-522450861 8895174927321243648 -522450861 +-538812082 7792036342592348160 -538812082 +-772236518 7250237407877382144 -772236518 +-805288503 7432428551399669760 -805288503 +-978892011 -7049618574399692800 -978892011 +1008698636 1719 1008698636 +1541249928 7659279803863146496 1541249928 +174310705 -6935038507792801792 174310705 +273256071 -9014145341570203648 273256071 +476704350 -7911421221625077760 476704350 +737149747 8283099811330506752 737149747 +810157660 2241 810157660 +851975276 504 851975276 +868714547 -7496839341561954304 868714547 +95356298 78 95356298 +NULL 1075 NULL +NULL 2607 NULL +NULL 3118 NULL +PREHOOK: query: select i,b,count(i) from vectortab2korc group by i,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select i,b,count(i) from vectortab2korc group by i,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +i b c2 +-1012329052 9182828596851990528 1 +-146961490 8100036735858401280 1 +-1730740504 302 1 +-1832606512 -8831091081349758976 1 +-1974777102 1774 1 +-2027812975 3764 1 +-407089271 661 1 +-522450861 8895174927321243648 1 +-538812082 7792036342592348160 1 +-772236518 7250237407877382144 1 +-805288503 7432428551399669760 1 +-978892011 -7049618574399692800 1 +1008698636 1719 1 +1541249928 7659279803863146496 1 +174310705 -6935038507792801792 1 +273256071 -9014145341570203648 1 +476704350 -7911421221625077760 1 +737149747 8283099811330506752 1 +810157660 2241 1 +851975276 504 1 +868714547 -7496839341561954304 1 +95356298 78 1 +NULL 1075 0 +NULL 2607 0 +NULL 3118 0 +PREHOOK: query: select si,b,count(*) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,count(*) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 1 +-1928 -6935038507792801792 1 +-21772 -8831091081349758976 1 +-28501 2241 1 +-30304 8100036735858401280 1 +-31395 302 1 +-9171 7432428551399669760 1 +-9609 7659279803863146496 1 +133 78 1 +14773 1774 1 +16195 8283099811330506752 1 +21091 9182828596851990528 1 +21849 -7496839341561954304 1 +23441 -7049618574399692800 1 +26557 661 1 +2671 504 1 +27922 3764 1 +30921 8895174927321243648 1 +725 3118 1 +7353 1719 1 +7401 -7911421221625077760 1 +8918 7250237407877382144 1 +NULL -9014145341570203648 1 +NULL 1075 1 +NULL 2607 1 +PREHOOK: query: select si,b,min(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,min(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,max(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,max(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,sum(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,sum(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 -10317 +-1928 -6935038507792801792 -1928 +-21772 -8831091081349758976 -21772 +-28501 2241 -28501 +-30304 8100036735858401280 -30304 +-31395 302 -31395 +-9171 7432428551399669760 -9171 +-9609 7659279803863146496 -9609 +133 78 133 +14773 1774 14773 +16195 8283099811330506752 16195 +21091 9182828596851990528 21091 +21849 -7496839341561954304 21849 +23441 -7049618574399692800 23441 +26557 661 26557 +2671 504 2671 +27922 3764 27922 +30921 8895174927321243648 30921 +725 3118 725 +7353 1719 7353 +7401 -7911421221625077760 7401 +8918 7250237407877382144 8918 +NULL -9014145341570203648 NULL +NULL 1075 NULL +NULL 2607 NULL +PREHOOK: query: select si,b,count(si) from vectortab2korc group by si,b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select si,b,count(si) from vectortab2korc group by si,b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +si b c2 +-10317 7792036342592348160 1 +-1928 -6935038507792801792 1 +-21772 -8831091081349758976 1 +-28501 2241 1 +-30304 8100036735858401280 1 +-31395 302 1 +-9171 7432428551399669760 1 +-9609 7659279803863146496 1 +133 78 1 +14773 1774 1 +16195 8283099811330506752 1 +21091 9182828596851990528 1 +21849 -7496839341561954304 1 +23441 -7049618574399692800 1 +26557 661 1 +2671 504 1 +27922 3764 1 +30921 8895174927321243648 1 +725 3118 1 +7353 1719 1 +7401 -7911421221625077760 1 +8918 7250237407877382144 1 +NULL -9014145341570203648 0 +NULL 1075 0 +NULL 2607 0 +PREHOOK: query: select t,i,count(*) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,count(*) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 1 +-19 95356298 1 +-29 -1974777102 1 +-35 NULL 1 +-4 273256071 1 +-43 851975276 1 +-46 810157660 1 +-57 -522450861 1 +-58 -1730740504 1 +-87 -1012329052 1 +-92 868714547 1 +111 NULL 1 +23 -805288503 1 +31 1541249928 1 +36 -538812082 1 +40 -1832606512 1 +47 -978892011 1 +48 476704350 1 +52 -772236518 1 +54 -146961490 1 +55 174310705 1 +7 NULL 1 +73 737149747 1 +95 -2027812975 1 +NULL -407089271 1 +PREHOOK: query: select t,i,min(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,min(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,max(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,max(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,sum(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,sum(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 -127 +-19 95356298 -19 +-29 -1974777102 -29 +-35 NULL -35 +-4 273256071 -4 +-43 851975276 -43 +-46 810157660 -46 +-57 -522450861 -57 +-58 -1730740504 -58 +-87 -1012329052 -87 +-92 868714547 -92 +111 NULL 111 +23 -805288503 23 +31 1541249928 31 +36 -538812082 36 +40 -1832606512 40 +47 -978892011 47 +48 476704350 48 +52 -772236518 52 +54 -146961490 54 +55 174310705 55 +7 NULL 7 +73 737149747 73 +95 -2027812975 95 +NULL -407089271 NULL +PREHOOK: query: select t,i,count(t) from vectortab2korc group by t,i +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select t,i,count(t) from vectortab2korc group by t,i +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +t i c2 +-127 1008698636 1 +-19 95356298 1 +-29 -1974777102 1 +-35 NULL 1 +-4 273256071 1 +-43 851975276 1 +-46 810157660 1 +-57 -522450861 1 +-58 -1730740504 1 +-87 -1012329052 1 +-92 868714547 1 +111 NULL 1 +23 -805288503 1 +31 1541249928 1 +36 -538812082 1 +40 -1832606512 1 +47 -978892011 1 +48 476704350 1 +52 -772236518 1 +54 -146961490 1 +55 174310705 1 +7 NULL 1 +73 737149747 1 +95 -2027812975 1 +NULL -407089271 0 diff --git ql/src/test/results/clientpositive/vector_groupby2.q.out ql/src/test/results/clientpositive/vector_groupby2.q.out new file mode 100644 index 0000000..70d6af4 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby2.q.out @@ -0,0 +1,6076 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Long Aggregations +explain +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Long Aggregations +explain +select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint) + outputColumnNames: b + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(b), max(b), sum(b), count(b) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: bigint), _col4 (type: bigint), _col5 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, count(*), min(b), max(b), sum(b), count(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 +-6917607783359897600 1 -6917607783359897600 -6917607783359897600 -6917607783359897600 1 +-6919476845891313664 1 -6919476845891313664 -6919476845891313664 -6919476845891313664 1 +-6920172215209426944 1 -6920172215209426944 -6920172215209426944 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371 7 +54 9 54 54 486 9 +55 10 55 55 550 10 +56 9 56 56 504 9 +57 6 57 57 342 6 +58 10 58 58 580 10 +59 10 59 59 590 10 +6 8 6 6 48 8 +60 6 60 60 360 6 +61 5 61 61 305 5 +62 9 62 62 558 9 +63 6 63 63 378 6 +64 8 64 64 512 8 +65 2 65 65 130 2 +66 6 66 66 396 6 +67 6 67 67 402 6 +68 6 68 68 408 6 +69 8 69 69 552 8 +7 11 7 7 77 11 +70 7 70 70 490 7 +71 6 71 71 426 6 +72 7 72 72 504 7 +73 8 73 73 584 8 +74 10 74 74 740 10 +75 8 75 75 600 8 +76 7 76 76 532 7 +77 8 77 77 616 8 +78 8 78 78 624 8 +79 11 79 79 869 11 +8 8 8 8 64 8 +80 8 80 80 640 8 +81 4 81 81 324 4 +82 9 82 82 738 9 +83 7 83 83 581 7 +84 9 84 84 756 9 +85 8 85 85 680 8 +86 7 86 86 602 7 +87 8 87 87 696 8 +88 5 88 88 440 5 +89 8 89 89 712 8 +9 6 9 9 54 6 +90 10 90 90 900 10 +91 5 91 91 455 5 +92 13 92 92 1196 13 +93 10 93 93 930 10 +94 5 94 94 470 5 +95 7 95 95 665 7 +96 12 96 96 1152 12 +97 9 97 97 873 9 +98 10 98 98 980 10 +99 3 99 99 297 3 +NULL 96 NULL NULL NULL 0 diff --git ql/src/test/results/clientpositive/vector_groupby3.q.out ql/src/test/results/clientpositive/vector_groupby3.q.out new file mode 100644 index 0000000..4fd35ff --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby3.q.out @@ -0,0 +1,127 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +CREATE TABLE orc_table_1(a INT, b INT) STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@orc_table_1 +POSTHOOK: query: -- SORT_QUERY_RESULTS + +CREATE TABLE orc_table_1(a INT, b INT) STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@orc_table_1 +PREHOOK: query: insert into table orc_table_1 values(12, 1),(5, 1), (9, 2),(12, 1) +PREHOOK: type: QUERY +PREHOOK: Input: default@values__tmp__table__1 +PREHOOK: Output: default@orc_table_1 +POSTHOOK: query: insert into table orc_table_1 values(12, 1),(5, 1), (9, 2),(12, 1) +POSTHOOK: type: QUERY +POSTHOOK: Input: default@values__tmp__table__1 +POSTHOOK: Output: default@orc_table_1 +POSTHOOK: Lineage: orc_table_1.a EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col1, type:string, comment:), ] +POSTHOOK: Lineage: orc_table_1.b EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col2, type:string, comment:), ] +_col0 _col1 +PREHOOK: query: -- +-- 2nd Column Long Aggregations +explain +select b, max(a) from orc_table_1 group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- 2nd Column Long Aggregations +explain +select b, max(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: orc_table_1 + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: int), a (type: int) + outputColumnNames: b, a + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(a) + keys: b (type: int) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: int) + sort order: + + Map-reduce partition columns: _col0 (type: int) + Statistics: Num rows: 4 Data size: 32 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: int) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0) + keys: KEY._col0 (type: int) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2 Data size: 16 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 2 Data size: 16 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, min(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, min(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 5 +2 9 +PREHOOK: query: select b, max(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, max(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 12 +2 9 +PREHOOK: query: select b, count(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, count(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 3 +2 1 +PREHOOK: query: select b, sum(a) from orc_table_1 group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +POSTHOOK: query: select b, sum(a) from orc_table_1 group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@orc_table_1 +#### A masked pattern was here #### +b c1 +1 29 +2 9 diff --git ql/src/test/results/clientpositive/vector_groupby4.q.out ql/src/test/results/clientpositive/vector_groupby4.q.out new file mode 100644 index 0000000..0f55317 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby4.q.out @@ -0,0 +1,3890 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Many Column Long Aggregations +explain +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Long Aggregations +explain +select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), t (type: tinyint), si (type: smallint), i (type: int) + outputColumnNames: b, t, si, i + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: tinyint), _col2 (type: bigint), _col3 (type: smallint), _col4 (type: smallint), _col5 (type: bigint), _col6 (type: bigint), _col7 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0), sum(VALUE._col1), min(VALUE._col2), max(VALUE._col3), sum(VALUE._col4), count(VALUE._col5), max(VALUE._col6) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(t), sum(t), min(si), max(si), sum(i), count(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 +-6917607783359897600 36 36 -1307 -1307 -603273425 1 -6917607783359897600 +-6919476845891313664 124 124 8811 8811 710856472 1 -6919476845891313664 +-6920172215209426944 115 115 -26832 -26832 -764412063 1 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-14 -14 15342 15342 -742707249 1 997 +999 107 107 11159 11159 -346607939 1 999 +NULL 127 -1065 -32371 32563 -9784926725 80 NULL +PREHOOK: query: select b, max(t), sum(t), min(t), min(si), sum(si), max(si), min(i), sum(i), count(i), max(i), max(b) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(t), sum(t), min(t), min(si), sum(si), max(si), min(i), sum(i), count(i), max(i), max(b) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 +-6917607783359897600 36 36 36 -1307 -1307 -1307 -603273425 -603273425 1 -603273425 -6917607783359897600 +-6919476845891313664 124 124 124 8811 8811 8811 710856472 710856472 1 710856472 -6919476845891313664 +-6920172215209426944 115 115 115 -26832 -26832 -26832 -764412063 -764412063 1 -764412063 -6920172215209426944 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ql/src/test/results/clientpositive/vector_groupby5.q.out @@ -0,0 +1,508 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k limit 25 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single Double Aggregations +explain +select d, count(*) from vectortab2korc group by d +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single Double Aggregations +explain +select d, count(*) from vectortab2korc group by d +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: d (type: double) + outputColumnNames: d + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: d (type: double) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 25 Data size: 11317 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: double) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 12 Data size: 5432 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select d, count(*) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(*) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 1 +-1387292.76 1 +-2081693.61 1 +-2096084.32 1 +-2506000.67 1 +-2636583.28 1 +-2738225.86 1 +-3116102.1 1 +-3860829.99 1 +-3997512.4 1 +-4409416.25 1 +-4926003.8 1 +-497550.81 1 +-51958.2 1 +-642668.92 1 +1118606.84 1 +1666710.2 1 +1848079.75 1 +2121137.53 1 +2409466.9 1 +2883584.5 1 +300734.43 1 +360339.96 1 +3882592.64 1 +393045.55 1 +PREHOOK: query: select d, min(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, min(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, max(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, max(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, sum(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, sum(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 -1305193.84 +-1387292.76 -1387292.76 +-2081693.61 -2081693.61 +-2096084.32 -2096084.32 +-2506000.67 -2506000.67 +-2636583.28 -2636583.28 +-2738225.86 -2738225.86 +-3116102.1 -3116102.1 +-3860829.99 -3860829.99 +-3997512.4 -3997512.4 +-4409416.25 -4409416.25 +-4926003.8 -4926003.8 +-497550.81 -497550.81 +-51958.2 -51958.2 +-642668.92 -642668.92 +1118606.84 1118606.84 +1666710.2 1666710.2 +1848079.75 1848079.75 +2121137.53 2121137.53 +2409466.9 2409466.9 +2883584.5 2883584.5 +300734.43 300734.43 +360339.96 360339.96 +3882592.64 3882592.64 +393045.55 393045.55 +PREHOOK: query: select d, count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 +-1305193.84 1 +-1387292.76 1 +-2081693.61 1 +-2096084.32 1 +-2506000.67 1 +-2636583.28 1 +-2738225.86 1 +-3116102.1 1 +-3860829.99 1 +-3997512.4 1 +-4409416.25 1 +-4926003.8 1 +-497550.81 1 +-51958.2 1 +-642668.92 1 +1118606.84 1 +1666710.2 1 +1848079.75 1 +2121137.53 1 +2409466.9 1 +2883584.5 1 +300734.43 1 +360339.96 1 +3882592.64 1 +393045.55 1 +PREHOOK: query: select f, count(*) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, count(*) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 1 +-14145.11 1 +-15490.77 1 +-23364.57 1 +-32124.85 1 +-35391.71 1 +-38119.53 1 +-38980.4 1 +-41874.29 1 +-4574.16 1 +-7382.06 1 +-7769.3 1 +11322.18 1 +1466.49 1 +21713.21 1 +22311.9 1 +32666.2 1 +32711.55 1 +35338.95 1 +36810.38 1 +38097.34 1 +4983.6 1 +5758.0 1 +5765.39 1 +8799.89 1 +PREHOOK: query: select f, min(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, min(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.93 +-14145.11 -14145.11 +-15490.77 -15490.77 +-23364.57 -23364.57 +-32124.85 -32124.85 +-35391.71 -35391.71 +-38119.53 -38119.53 +-38980.4 -38980.4 +-41874.29 -41874.29 +-4574.16 -4574.16 +-7382.06 -7382.06 +-7769.3 -7769.3 +11322.18 11322.18 +1466.49 1466.49 +21713.21 21713.21 +22311.9 22311.9 +32666.2 32666.2 +32711.55 32711.55 +35338.95 35338.95 +36810.38 36810.38 +38097.34 38097.34 +4983.6 4983.6 +5758.0 5758.0 +5765.39 5765.39 +8799.89 8799.89 +PREHOOK: query: select f, max(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, max(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.93 +-14145.11 -14145.11 +-15490.77 -15490.77 +-23364.57 -23364.57 +-32124.85 -32124.85 +-35391.71 -35391.71 +-38119.53 -38119.53 +-38980.4 -38980.4 +-41874.29 -41874.29 +-4574.16 -4574.16 +-7382.06 -7382.06 +-7769.3 -7769.3 +11322.18 11322.18 +1466.49 1466.49 +21713.21 21713.21 +22311.9 22311.9 +32666.2 32666.2 +32711.55 32711.55 +35338.95 35338.95 +36810.38 36810.38 +38097.34 38097.34 +4983.6 4983.6 +5758.0 5758.0 +5765.39 5765.39 +8799.89 8799.89 +PREHOOK: query: select f, sum(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, sum(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 -12519.9296875 +-14145.11 -14145.1103515625 +-15490.77 -15490.76953125 +-23364.57 -23364.5703125 +-32124.85 -32124.849609375 +-35391.71 -35391.7109375 +-38119.53 -38119.53125 +-38980.4 -38980.3984375 +-41874.29 -41874.2890625 +-4574.16 -4574.16015625 +-7382.06 -7382.06005859375 +-7769.3 -7769.2998046875 +11322.18 11322.1796875 +1466.49 1466.489990234375 +21713.21 21713.2109375 +22311.9 22311.900390625 +32666.2 32666.19921875 +32711.55 32711.55078125 +35338.95 35338.94921875 +36810.38 36810.37890625 +38097.34 38097.33984375 +4983.6 4983.60009765625 +5758.0 5758.0 +5765.39 5765.39013671875 +8799.89 8799.8896484375 +PREHOOK: query: select f, count(f) from vectortab2korc group by f +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select f, count(f) from vectortab2korc group by f +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +f c1 +-12519.93 1 +-14145.11 1 +-15490.77 1 +-23364.57 1 +-32124.85 1 +-35391.71 1 +-38119.53 1 +-38980.4 1 +-41874.29 1 +-4574.16 1 +-7382.06 1 +-7769.3 1 +11322.18 1 +1466.49 1 +21713.21 1 +22311.9 1 +32666.2 1 +32711.55 1 +35338.95 1 +36810.38 1 +38097.34 1 +4983.6 1 +5758.0 1 +5765.39 1 +8799.89 1 diff --git ql/src/test/results/clientpositive/vector_groupby6.q.out ql/src/test/results/clientpositive/vector_groupby6.q.out new file mode 100644 index 0000000..52c9a13 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby6.q.out @@ -0,0 +1,3991 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- All Double Aggregations +explain +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- All Double Aggregations +explain +select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: d (type: double) + outputColumnNames: d + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count(), min(d), max(d), sum(d), count(d) + keys: d (type: double) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: double) + sort order: + + Map-reduce partition columns: _col0 (type: double) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: double), _col3 (type: double), _col4 (type: double), _col5 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0), min(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), count(VALUE._col4) + keys: KEY._col0 (type: double) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select d, count(*), min(d), max(d), sum(d), count(d) from vectortab2korc group by d +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +d c1 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NULL 0 diff --git ql/src/test/results/clientpositive/vector_groupby7.q.out ql/src/test/results/clientpositive/vector_groupby7.q.out new file mode 100644 index 0000000..3da077a --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby7.q.out @@ -0,0 +1,3899 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f3 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +_col0 _col1 _col2 _col3 _col4 _col5 _col6 _col7 _col8 _col9 _col10 _col11 _col12 _col13 _col14 _col15 +PREHOOK: query: -- +-- Many Column Double Aggregations +explain +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Double Aggregations +explain +select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), d2 (type: double), f (type: float), f2 (type: float), d (type: double) + outputColumnNames: b, d2, f, f2, d + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: double), _col2 (type: double), _col3 (type: float), _col4 (type: float), _col5 (type: double), _col6 (type: bigint), _col7 (type: double) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: max(VALUE._col0), sum(VALUE._col1), min(VALUE._col2), max(VALUE._col3), sum(VALUE._col4), count(VALUE._col5), max(VALUE._col6) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7 + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 +-6917607783359897600 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max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 +-6917607783359897600 -6.9197441481716326E22 -6.9197441481716326E22 -6.9197441481716326E22 20495.55 20495.55078125 -6.9197441481716326E22 -2636304.8 462.239990234375 1 462.24 -717022.0 +-6919476845891313664 -6.92161378792563E22 -6.92161378792563E22 -6.92161378792563E22 45495.65 45495.6484375 -6.92161378792563E22 3106442.8 1592.1600341796875 1 1592.16 -1060105.47 +-6920172215209426944 -6.92230937199465E22 -6.92230937199465E22 -6.92230937199465E22 17394.03 17394.029296875 -6.92230937199465E22 -3340480.5 1476.5999755859375 1 1476.6 561210.22 +-6921654334727036928 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NULL NULL -44985.09 -146391.67810058594 NULL -9043450.0 -13674.600257873535 83 1630.68 4997627.14 diff --git ql/src/test/results/clientpositive/vector_groupby8.q.out ql/src/test/results/clientpositive/vector_groupby8.q.out new file mode 100644 index 0000000..0ac32fe --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby8.q.out @@ -0,0 +1,3899 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + f2 float, + si smallint, + i int, + f3 float, + b bigint, + d2 double, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT t, (cast(t as float) * 12.84) as f2, si, i, (cast(i as float) * 0.00437) as f3, b, (cast(b as double) * 10003.0883) as d2, f, d, dc, bo, s, s2, ts, ts2, dt FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f2 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f3 EXPRESSION [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +_col0 _col1 _col2 _col3 _col4 _col5 _col6 _col7 _col8 _col9 _col10 _col11 _col12 _col13 _col14 _col15 +PREHOOK: query: -- +-- Many Column Long, Double Aggregations +explain +select b, min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Many Column Long, Double Aggregations +explain +select b, min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: b (type: bigint), d2 (type: double), f (type: float), f2 (type: float), d (type: double) + outputColumnNames: b, d2, f, f2, d + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: min(b), max(b), max(d2), sum(d2), min(f), max(f), sum(f2), count(f2), max(d) + keys: b (type: bigint) + mode: hash + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: bigint) + sort order: + + Map-reduce partition columns: _col0 (type: bigint) + Statistics: Num rows: 2000 Data size: 949296 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: double), _col4 (type: double), _col5 (type: float), _col6 (type: float), _col7 (type: double), _col8 (type: bigint), _col9 (type: double) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: min(VALUE._col0), max(VALUE._col1), max(VALUE._col2), sum(VALUE._col3), min(VALUE._col4), max(VALUE._col5), sum(VALUE._col6), count(VALUE._col7), max(VALUE._col8) + keys: KEY._col0 (type: bigint) + mode: mergepartial + outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9 + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 474648 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select b, max(d2), min(si), sum(d2), min(f), count(t), max(f), sum(f2), count(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, 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-119465.75 +997 9973079.0351 15342 9973079.0351 -30055.88 1 -30055.88 -179.75999450683594 1 3904057.55 +999 9993085.2117 11159 9993085.2117 -24238.72 1 -24238.72 1373.8800048828125 1 794247.13 +NULL NULL -32371 NULL -44985.09 83 49143.26 -13674.600257873535 83 4997627.14 +PREHOOK: query: select b, min(t), max(t), sum(t), count(t), max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select b, min(t), max(t), sum(t), count(t), max(d2), sum(d2), min(d2), min(f), sum(f), max(d2), min(f3), sum(f2), count(f2), max(f2), max(d) from vectortab2korc group by b +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +b c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 +-6917607783359897600 36 36 36 1 -6.9197441481716326E22 -6.9197441481716326E22 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9793023.4457 9793023.4457 -34858.06 -34858.05859375 9793023.4457 4467079.0 1579.3199462890625 1 1579.32 -4611759.94 +982 -98 -98 -98 1 9823032.7106 9823032.7106 9823032.7106 39755.57 39755.5703125 9823032.7106 -3649817.5 -1258.3199462890625 1 -1258.32 1372628.14 +987 NULL NULL NULL 0 9873048.152099999 9873048.152099999 9873048.152099999 -5972.19 -5972.18994140625 9873048.152099999 7900425.5 NULL 0 NULL -119465.75 +997 -14 -14 -14 1 9973079.0351 9973079.0351 9973079.0351 -30055.88 -30055.880859375 9973079.0351 -3245630.8 -179.75999450683594 1 -179.76 3904057.55 +999 107 107 107 1 9993085.2117 9993085.2117 9993085.2117 -24238.72 -24238.720703125 9993085.2117 -1514676.6 1373.8800048828125 1 1373.88 794247.13 +NULL -121 127 -1065 83 NULL NULL NULL -44985.09 -146391.67810058594 NULL -9043450.0 -13674.600257873535 83 1630.68 4997627.14 diff --git ql/src/test/results/clientpositive/vector_groupby9.q.out ql/src/test/results/clientpositive/vector_groupby9.q.out new file mode 100644 index 0000000..39a94d6 --- /dev/null +++ ql/src/test/results/clientpositive/vector_groupby9.q.out @@ -0,0 +1,240 @@ +PREHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: -- SORT_QUERY_RESULTS + +create table vectortab2k( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' +STORED AS TEXTFILE +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +PREHOOK: type: LOAD +#### A masked pattern was here #### +PREHOOK: Output: default@vectortab2k +POSTHOOK: query: LOAD DATA LOCAL INPATH '../../data/files/vectortab2k' OVERWRITE INTO TABLE vectortab2k +POSTHOOK: type: LOAD +#### A masked pattern was here #### +POSTHOOK: Output: default@vectortab2k +PREHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +PREHOOK: type: CREATETABLE +PREHOOK: Output: database:default +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: create table vectortab2korc( + t tinyint, + si smallint, + i int, + b bigint, + f float, + d double, + dc decimal(38,18), + bo boolean, + s string, + s2 string, + ts timestamp, + ts2 timestamp, + dt date) +STORED AS ORC +POSTHOOK: type: CREATETABLE +POSTHOOK: Output: database:default +POSTHOOK: Output: default@vectortab2korc +PREHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2k +PREHOOK: Output: default@vectortab2korc +POSTHOOK: query: INSERT INTO TABLE vectortab2korc SELECT * FROM vectortab2k +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2k +POSTHOOK: Output: default@vectortab2korc +POSTHOOK: Lineage: vectortab2korc.b SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:b, type:bigint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.bo SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:bo, type:boolean, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.d SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:d, type:double, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dc SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dc, type:decimal(38,18), comment:null), ] +POSTHOOK: Lineage: vectortab2korc.dt SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:dt, type:date, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.f SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:f, type:float, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.i SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:i, type:int, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.s2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:s2, type:string, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.si SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:si, type:smallint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.t SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:t, type:tinyint, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts, type:timestamp, comment:null), ] +POSTHOOK: Lineage: vectortab2korc.ts2 SIMPLE [(vectortab2k)vectortab2k.FieldSchema(name:ts2, type:timestamp, comment:null), ] +vectortab2k.t vectortab2k.si vectortab2k.i vectortab2k.b vectortab2k.f vectortab2k.d vectortab2k.dc vectortab2k.bo vectortab2k.s vectortab2k.s2 vectortab2k.ts vectortab2k.ts2 vectortab2k.dt +PREHOOK: query: -- +-- Single String Aggregations (not including min, max, sum) +explain +select s, count(*) from vectortab2korc group by s +PREHOOK: type: QUERY +POSTHOOK: query: -- +-- Single String Aggregations (not including min, max, sum) +explain +select s, count(*) from vectortab2korc group by s +POSTHOOK: type: QUERY +Explain +STAGE DEPENDENCIES: + Stage-1 is a root stage + Stage-0 depends on stages: Stage-1 + +STAGE PLANS: + Stage: Stage-1 + Map Reduce + Map Operator Tree: + TableScan + alias: vectortab2korc + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Select Operator + expressions: s (type: string) + outputColumnNames: s + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Group By Operator + aggregations: count() + keys: s (type: string) + mode: hash + outputColumnNames: _col0, _col1 + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + Reduce Output Operator + key expressions: _col0 (type: string) + sort order: + + Map-reduce partition columns: _col0 (type: string) + Statistics: Num rows: 2000 Data size: 918712 Basic stats: COMPLETE Column stats: NONE + value expressions: _col1 (type: bigint) + Execution mode: vectorized + Reduce Operator Tree: + Group By Operator + aggregations: count(VALUE._col0) + keys: KEY._col0 (type: string) + mode: mergepartial + outputColumnNames: _col0, _col1 + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + File Output Operator + compressed: false + Statistics: Num rows: 1000 Data size: 459356 Basic stats: COMPLETE Column stats: NONE + table: + input format: org.apache.hadoop.mapred.TextInputFormat + output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat + serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe + + Stage: Stage-0 + Fetch Operator + limit: -1 + Processor Tree: + ListSink + +PREHOOK: query: select s, count(*) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(*) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 + 94 +american history 80 +biology 91 +chemistry 79 +debate 71 +education 83 +forestry 76 +geology 76 +history 74 +industrial engineering 63 +joggying 73 +kindergarten 61 +linguistics 69 +mathematics 75 +nap time 56 +opthamology 75 +philosophy 66 +quiet hour 67 +religion 76 +study skills 77 +topology 69 +undecided 67 +values clariffication 84 +wind surfing 90 +xylophone band 71 +yard duty 67 +zync studies 70 +PREHOOK: query: select s, count(b) from vectortab2korc group by s +PREHOOK: type: QUERY +PREHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +POSTHOOK: query: select s, count(b) from vectortab2korc group by s +POSTHOOK: type: QUERY +POSTHOOK: Input: default@vectortab2korc +#### A masked pattern was here #### +s c1 + 87 +american history 78 +biology 86 +chemistry 75 +debate 70 +education 80 +forestry 75 +geology 73 +history 71 +industrial engineering 61 +joggying 70 +kindergarten 58 +linguistics 63 +mathematics 73 +nap time 52 +opthamology 71 +philosophy 66 +quiet hour 63 +religion 74 +study skills 75 +topology 67 +undecided 64 +values clariffication 77 +wind surfing 88 +xylophone band 70 +yard duty 62 +zync studies 68 diff --git ql/src/test/results/clientpositive/vector_outer_join0.q.out ql/src/test/results/clientpositive/vector_outer_join0.q.out index 53face9..4dcd875 100644 --- ql/src/test/results/clientpositive/vector_outer_join0.q.out +++ ql/src/test/results/clientpositive/vector_outer_join0.q.out @@ -24,6 +24,7 @@ POSTHOOK: Input: default@values__tmp__table__1 POSTHOOK: Output: default@orc_table_1 POSTHOOK: Lineage: orc_table_1.a EXPRESSION [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col2, type:string, comment:), ] POSTHOOK: Lineage: orc_table_1.v1 SIMPLE [(values__tmp__table__1)values__tmp__table__1.FieldSchema(name:tmp_values_col1, type:string, comment:), ] +_col0 _col1 PREHOOK: query: insert into table orc_table_2 values (0, "ZERO"),(2, "TWO"), (3, "THREE"),(null, ""),(4, "FOUR"),(null, "") PREHOOK: type: QUERY PREHOOK: Input: default@values__tmp__table__2 @@ -34,6 +35,7 @@ POSTHOOK: Input: default@values__tmp__table__2 POSTHOOK: Output: default@orc_table_2 POSTHOOK: Lineage: orc_table_2.c EXPRESSION [(values__tmp__table__2)values__tmp__table__2.FieldSchema(name:tmp_values_col1, type:string, comment:), ] POSTHOOK: Lineage: orc_table_2.v2 SIMPLE [(values__tmp__table__2)values__tmp__table__2.FieldSchema(name:tmp_values_col2, type:string, comment:), ] +_col0 _col1 PREHOOK: query: select * from orc_table_1 PREHOOK: type: QUERY PREHOOK: Input: default@orc_table_1 @@ -42,6 +44,7 @@ POSTHOOK: query: select * from orc_table_1 POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 #### A masked pattern was here #### +orc_table_1.v1 orc_table_1.a NULL NULL one 1 @@ -56,6 +59,7 @@ POSTHOOK: query: select * from orc_table_2 POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +orc_table_2.c orc_table_2.v2 0 ZERO 2 TWO 3 THREE @@ -68,6 +72,7 @@ PREHOOK: type: QUERY POSTHOOK: query: explain select t1.v1, t1.a, t2.c, t2.v2 from orc_table_1 t1 left outer join orc_table_2 t2 on t1.a = t2.c POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-4 is a root stage Stage-3 depends on stages: Stage-4 @@ -143,6 +148,7 @@ POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +t1.v1 t1.a t2.c t2.v2 NULL NULL NULL NULL NULL NULL one 1 NULL NULL @@ -155,6 +161,7 @@ PREHOOK: type: QUERY POSTHOOK: query: explain select t1.v1, t1.a, t2.c, t2.v2 from orc_table_1 t1 right outer join orc_table_2 t2 on t1.a = t2.c POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-4 is a root stage Stage-3 depends on stages: Stage-4 @@ -230,6 +237,7 @@ POSTHOOK: type: QUERY POSTHOOK: Input: default@orc_table_1 POSTHOOK: Input: default@orc_table_2 #### A masked pattern was here #### +t1.v1 t1.a t2.c t2.v2 NULL NULL 0 ZERO NULL NULL 4 FOUR NULL NULL NULL diff --git ql/src/test/results/clientpositive/vector_outer_join3.q.out ql/src/test/results/clientpositive/vector_outer_join3.q.out index 3345247..d778d87 100644 --- ql/src/test/results/clientpositive/vector_outer_join3.q.out +++ ql/src/test/results/clientpositive/vector_outer_join3.q.out @@ -10,6 +10,7 @@ POSTHOOK: type: CREATETABLE_AS_SELECT POSTHOOK: Input: default@alltypesorc POSTHOOK: Output: database:default POSTHOOK: Output: default@small_alltypesorc1a +alltypesorc.ctinyint alltypesorc.csmallint alltypesorc.cint alltypesorc.cbigint alltypesorc.cfloat alltypesorc.cdouble alltypesorc.cstring1 alltypesorc.cstring2 alltypesorc.ctimestamp1 alltypesorc.ctimestamp2 alltypesorc.cboolean1 alltypesorc.cboolean2 PREHOOK: query: create table small_alltypesorc2a as select * from alltypesorc where cint is null and cstring1 is not null order by ctinyint, csmallint, cint, cbigint, cfloat, cdouble, cstring1, cstring2, ctimestamp1, ctimestamp2, cboolean1, cboolean2 limit 5 PREHOOK: type: CREATETABLE_AS_SELECT PREHOOK: Input: default@alltypesorc @@ -20,6 +21,7 @@ POSTHOOK: type: CREATETABLE_AS_SELECT POSTHOOK: Input: default@alltypesorc POSTHOOK: Output: database:default POSTHOOK: Output: default@small_alltypesorc2a +alltypesorc.ctinyint alltypesorc.csmallint alltypesorc.cint alltypesorc.cbigint alltypesorc.cfloat alltypesorc.cdouble alltypesorc.cstring1 alltypesorc.cstring2 alltypesorc.ctimestamp1 alltypesorc.ctimestamp2 alltypesorc.cboolean1 alltypesorc.cboolean2 PREHOOK: query: create table small_alltypesorc3a as select * from alltypesorc where cint is not null and cstring1 is null order by ctinyint, csmallint, cint, cbigint, cfloat, cdouble, cstring1, cstring2, ctimestamp1, ctimestamp2, cboolean1, cboolean2 limit 5 PREHOOK: type: CREATETABLE_AS_SELECT PREHOOK: Input: default@alltypesorc @@ -30,6 +32,7 @@ POSTHOOK: type: CREATETABLE_AS_SELECT POSTHOOK: Input: default@alltypesorc POSTHOOK: Output: database:default POSTHOOK: Output: default@small_alltypesorc3a +alltypesorc.ctinyint alltypesorc.csmallint alltypesorc.cint alltypesorc.cbigint alltypesorc.cfloat alltypesorc.cdouble alltypesorc.cstring1 alltypesorc.cstring2 alltypesorc.ctimestamp1 alltypesorc.ctimestamp2 alltypesorc.cboolean1 alltypesorc.cboolean2 PREHOOK: query: create table small_alltypesorc4a as select * from alltypesorc where cint is null and cstring1 is null order by ctinyint, csmallint, cint, cbigint, cfloat, cdouble, cstring1, cstring2, ctimestamp1, ctimestamp2, cboolean1, cboolean2 limit 5 PREHOOK: type: CREATETABLE_AS_SELECT PREHOOK: Input: default@alltypesorc @@ -40,6 +43,7 @@ POSTHOOK: type: CREATETABLE_AS_SELECT POSTHOOK: Input: default@alltypesorc POSTHOOK: Output: database:default POSTHOOK: Output: default@small_alltypesorc4a +alltypesorc.ctinyint alltypesorc.csmallint alltypesorc.cint alltypesorc.cbigint alltypesorc.cfloat alltypesorc.cdouble alltypesorc.cstring1 alltypesorc.cstring2 alltypesorc.ctimestamp1 alltypesorc.ctimestamp2 alltypesorc.cboolean1 alltypesorc.cboolean2 PREHOOK: query: select * from small_alltypesorc1a PREHOOK: type: QUERY PREHOOK: Input: default@small_alltypesorc1a @@ -48,6 +52,7 @@ POSTHOOK: query: select * from small_alltypesorc1a POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc1a #### A masked pattern was here #### +small_alltypesorc1a.ctinyint small_alltypesorc1a.csmallint small_alltypesorc1a.cint small_alltypesorc1a.cbigint small_alltypesorc1a.cfloat small_alltypesorc1a.cdouble small_alltypesorc1a.cstring1 small_alltypesorc1a.cstring2 small_alltypesorc1a.ctimestamp1 small_alltypesorc1a.ctimestamp2 small_alltypesorc1a.cboolean1 small_alltypesorc1a.cboolean2 NULL NULL -1015272448 -1887561756 NULL NULL jTQ68531mP 4hA4KQj2vD3fI6gX82220d NULL 1969-12-31 15:59:45.854 false false NULL NULL -850295959 -1887561756 NULL NULL WMIgGA73 4hA4KQj2vD3fI6gX82220d NULL 1969-12-31 16:00:00.348 false false NULL NULL -886426182 -1887561756 NULL NULL 0i88xYq3gx1nW4vKjp7vBp3 4hA4KQj2vD3fI6gX82220d NULL 1969-12-31 16:00:04.472 true false @@ -61,6 +66,7 @@ POSTHOOK: query: select * from small_alltypesorc2a POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc2a #### A masked pattern was here #### +small_alltypesorc2a.ctinyint small_alltypesorc2a.csmallint small_alltypesorc2a.cint small_alltypesorc2a.cbigint small_alltypesorc2a.cfloat small_alltypesorc2a.cdouble small_alltypesorc2a.cstring1 small_alltypesorc2a.cstring2 small_alltypesorc2a.ctimestamp1 small_alltypesorc2a.ctimestamp2 small_alltypesorc2a.cboolean1 small_alltypesorc2a.cboolean2 -51 NULL NULL -1731061911 -51.0 NULL Pw53BBJ yL443x2437PO5Hv1U3lCjq2D 1969-12-31 16:00:08.451 NULL true false -51 NULL NULL -1846191223 -51.0 NULL Ul085f84S33Xd32u x1JC58g0Ukp 1969-12-31 16:00:08.451 NULL true true -51 NULL NULL -1874052220 -51.0 NULL c61B47I604gymFJ sjWQS78 1969-12-31 16:00:08.451 NULL false false @@ -74,6 +80,7 @@ POSTHOOK: query: select * from small_alltypesorc3a POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc3a #### A masked pattern was here #### +small_alltypesorc3a.ctinyint small_alltypesorc3a.csmallint small_alltypesorc3a.cint small_alltypesorc3a.cbigint small_alltypesorc3a.cfloat small_alltypesorc3a.cdouble small_alltypesorc3a.cstring1 small_alltypesorc3a.cstring2 small_alltypesorc3a.ctimestamp1 small_alltypesorc3a.ctimestamp2 small_alltypesorc3a.cboolean1 small_alltypesorc3a.cboolean2 -51 NULL -31312632 1086455747 -51.0 NULL NULL Bc7xt12568c451o64LF5 1969-12-31 16:00:08.451 NULL NULL true -51 NULL -337975743 608681041 -51.0 NULL NULL Ih2r28o6 1969-12-31 16:00:08.451 NULL NULL true -51 NULL -413196097 -306198070 -51.0 NULL NULL F53QcSDPpxYF1Ub 1969-12-31 16:00:08.451 NULL NULL false @@ -87,6 +94,7 @@ POSTHOOK: query: select * from small_alltypesorc4a POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc4a #### A masked pattern was here #### +small_alltypesorc4a.ctinyint small_alltypesorc4a.csmallint small_alltypesorc4a.cint small_alltypesorc4a.cbigint small_alltypesorc4a.cfloat small_alltypesorc4a.cdouble small_alltypesorc4a.cstring1 small_alltypesorc4a.cstring2 small_alltypesorc4a.ctimestamp1 small_alltypesorc4a.ctimestamp2 small_alltypesorc4a.cboolean1 small_alltypesorc4a.cboolean2 -64 -7196 NULL -1615920595 -64.0 -7196.0 NULL X5rDjl 1969-12-31 16:00:11.912 1969-12-31 15:59:58.174 NULL false -64 -7196 NULL -1639157869 -64.0 -7196.0 NULL IJ0Oj7qAiqNGsN7gn 1969-12-31 16:00:01.785 1969-12-31 15:59:58.174 NULL false -64 -7196 NULL -527203677 -64.0 -7196.0 NULL JBE4H5RoK412Cs260I72 1969-12-31 15:59:50.184 1969-12-31 15:59:58.174 NULL true @@ -122,6 +130,7 @@ POSTHOOK: Input: default@small_alltypesorc3a POSTHOOK: Input: default@small_alltypesorc4a POSTHOOK: Output: database:default POSTHOOK: Output: default@small_alltypesorc_a +q.ctinyint q.csmallint q.cint q.cbigint q.cfloat q.cdouble q.cstring1 q.cstring2 q.ctimestamp1 q.ctimestamp2 q.cboolean1 q.cboolean2 PREHOOK: query: ANALYZE TABLE small_alltypesorc_a COMPUTE STATISTICS PREHOOK: type: QUERY PREHOOK: Input: default@small_alltypesorc_a @@ -130,6 +139,7 @@ POSTHOOK: query: ANALYZE TABLE small_alltypesorc_a COMPUTE STATISTICS POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a POSTHOOK: Output: default@small_alltypesorc_a +small_alltypesorc_a.ctinyint small_alltypesorc_a.csmallint small_alltypesorc_a.cint small_alltypesorc_a.cbigint small_alltypesorc_a.cfloat small_alltypesorc_a.cdouble small_alltypesorc_a.cstring1 small_alltypesorc_a.cstring2 small_alltypesorc_a.ctimestamp1 small_alltypesorc_a.ctimestamp2 small_alltypesorc_a.cboolean1 small_alltypesorc_a.cboolean2 PREHOOK: query: ANALYZE TABLE small_alltypesorc_a COMPUTE STATISTICS FOR COLUMNS PREHOOK: type: QUERY PREHOOK: Input: default@small_alltypesorc_a @@ -138,6 +148,7 @@ POSTHOOK: query: ANALYZE TABLE small_alltypesorc_a COMPUTE STATISTICS FOR COLUMN POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a #### A masked pattern was here #### +_c0 _c1 _c2 _c3 _c4 _c5 _c6 _c7 _c8 _c9 _c10 _c11 PREHOOK: query: select * from small_alltypesorc_a PREHOOK: type: QUERY PREHOOK: Input: default@small_alltypesorc_a @@ -146,6 +157,7 @@ POSTHOOK: query: select * from small_alltypesorc_a POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a #### A masked pattern was here #### +small_alltypesorc_a.ctinyint small_alltypesorc_a.csmallint small_alltypesorc_a.cint small_alltypesorc_a.cbigint small_alltypesorc_a.cfloat small_alltypesorc_a.cdouble small_alltypesorc_a.cstring1 small_alltypesorc_a.cstring2 small_alltypesorc_a.ctimestamp1 small_alltypesorc_a.ctimestamp2 small_alltypesorc_a.cboolean1 small_alltypesorc_a.cboolean2 -51 NULL -31312632 1086455747 -51.0 NULL NULL Bc7xt12568c451o64LF5 1969-12-31 16:00:08.451 NULL NULL true -51 NULL -337975743 608681041 -51.0 NULL NULL Ih2r28o6 1969-12-31 16:00:08.451 NULL NULL true -51 NULL -413196097 -306198070 -51.0 NULL NULL F53QcSDPpxYF1Ub 1969-12-31 16:00:08.451 NULL NULL false @@ -184,6 +196,7 @@ left outer join small_alltypesorc_a hd on hd.cstring1 = c.cstring1 ) t1 POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-8 is a root stage Stage-3 depends on stages: Stage-8 @@ -306,6 +319,7 @@ left outer join small_alltypesorc_a hd POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a #### A masked pattern was here #### +c0 20 PREHOOK: query: explain select count(*) from (select c.cstring1 @@ -325,6 +339,7 @@ left outer join small_alltypesorc_a hd on hd.cstring1 = c.cstring1 ) t1 POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-8 is a root stage Stage-3 depends on stages: Stage-8 @@ -447,6 +462,7 @@ left outer join small_alltypesorc_a hd POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a #### A masked pattern was here #### +c0 28 PREHOOK: query: explain select count(*) from (select c.cstring1 @@ -466,6 +482,7 @@ left outer join small_alltypesorc_a hd on hd.cstring1 = c.cstring1 and hd.cint = c.cint ) t1 POSTHOOK: type: QUERY +Explain STAGE DEPENDENCIES: Stage-8 is a root stage Stage-3 depends on stages: Stage-8 @@ -588,4 +605,5 @@ left outer join small_alltypesorc_a hd POSTHOOK: type: QUERY POSTHOOK: Input: default@small_alltypesorc_a #### A masked pattern was here #### +c0 28 diff --git serde/src/java/org/apache/hadoop/hive/serde2/binarysortable/fast/BinarySortableDeserializeRead.java serde/src/java/org/apache/hadoop/hive/serde2/binarysortable/fast/BinarySortableDeserializeRead.java index c6ff748..cdcdc6d 100644 --- serde/src/java/org/apache/hadoop/hive/serde2/binarysortable/fast/BinarySortableDeserializeRead.java +++ serde/src/java/org/apache/hadoop/hive/serde2/binarysortable/fast/BinarySortableDeserializeRead.java @@ -91,8 +91,8 @@ /* * Use this constructor when only ascending sort order is used. */ - public BinarySortableDeserializeRead(PrimitiveTypeInfo[] primitiveTypeInfos) { - this(primitiveTypeInfos, null); + public BinarySortableDeserializeRead(TypeInfo[] typeInfos) { + this(typeInfos, null); } public BinarySortableDeserializeRead(TypeInfo[] typeInfos, @@ -133,6 +133,14 @@ public void set(byte[] bytes, int offset, int length) { } /* + * Get context for current row being read for error reporting. + */ + @Override + public String getCurrentContext() { + throw new RuntimeException("Not implemented yet"); + } + + /* * Reads the NULL information for a field. * * @return Returns true when the field is NULL; reading is positioned to the next field. diff --git serde/src/java/org/apache/hadoop/hive/serde2/fast/DeserializeRead.java serde/src/java/org/apache/hadoop/hive/serde2/fast/DeserializeRead.java index c2b0cfc..5101e7d 100644 --- serde/src/java/org/apache/hadoop/hive/serde2/fast/DeserializeRead.java +++ serde/src/java/org/apache/hadoop/hive/serde2/fast/DeserializeRead.java @@ -65,6 +65,11 @@ void set(byte[] bytes, int offset, int length); /* + * Get context for current row being read for error reporting. + */ + String getCurrentContext(); + + /* * Reads the NULL information for a field. * * @return Return true when the field is NULL; reading is positioned to the next field. diff --git serde/src/java/org/apache/hadoop/hive/serde2/lazy/fast/LazySimpleDeserializeRead.java serde/src/java/org/apache/hadoop/hive/serde2/lazy/fast/LazySimpleDeserializeRead.java index f44a84b..ae380d0 100644 --- serde/src/java/org/apache/hadoop/hive/serde2/lazy/fast/LazySimpleDeserializeRead.java +++ serde/src/java/org/apache/hadoop/hive/serde2/lazy/fast/LazySimpleDeserializeRead.java @@ -155,6 +155,14 @@ public void set(byte[] bytes, int offset, int length) { } /* + * Get context for current row being read for error reporting. + */ + @Override + public String getCurrentContext() { + throw new RuntimeException("Not implemented yet"); + } + + /* * Reads the NULL information for a field. * * @return Returns true when the field is NULL; reading is positioned to the next field. diff --git serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinaryDeserializeRead.java serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinaryDeserializeRead.java index c5f0730..87eee0c 100644 --- serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinaryDeserializeRead.java +++ serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinaryDeserializeRead.java @@ -67,6 +67,7 @@ private int offset; private int end; private int fieldCount; + private int saveFieldStart; private int fieldIndex; private byte nullByte; @@ -110,12 +111,38 @@ private LazyBinaryDeserializeRead() { public void set(byte[] bytes, int offset, int length) { this.bytes = bytes; this.offset = offset; + saveFieldStart = -1; start = offset; end = offset + length; fieldIndex = 0; } /* + * Get context for current row being read for error reporting. + */ + @Override + public String getCurrentContext() { + StringBuilder sb = new StringBuilder(); + sb.append("field "); + sb.append(fieldIndex - 1); + sb.append(" of "); + sb.append(fieldCount); + sb.append(" fields"); + + sb.append(", row start "); + sb.append(start); + sb.append(", row end "); + sb.append(end); + + sb.append(", field start "); + sb.append(saveFieldStart); + sb.append(", field length"); + sb.append(offset - saveFieldStart); + + return sb.toString(); + } + + /* * Reads the NULL information for a field. * * @return Returns true when the field is NULL; reading is positioned to the next field. @@ -123,6 +150,7 @@ public void set(byte[] bytes, int offset, int length) { */ @Override public boolean readCheckNull() throws IOException { + saveFieldStart = offset; if (fieldIndex >= fieldCount) { // Reading beyond the specified field count produces NULL. if (!readBeyondConfiguredFieldsWarned) { @@ -453,6 +481,9 @@ public void readString(ReadStringResults readStringResults) throws IOException { if (offset > end) { warnBeyondEof(); } + if (tempVInt.value < 0) { + throw new IOException("Bad string length " + tempVInt.value); + } int saveStart = offset; int length = tempVInt.value; offset += length; diff --git serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinarySerializeWrite.java serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinarySerializeWrite.java index 8f81df6..9ef1eda 100644 --- serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinarySerializeWrite.java +++ serde/src/java/org/apache/hadoop/hive/serde2/lazybinary/fast/LazyBinarySerializeWrite.java @@ -45,7 +45,7 @@ * * This is an alternative way to serialize than what is provided by LazyBinarySerDe. */ -public class LazyBinarySerializeWrite implements SerializeWrite { +public final class LazyBinarySerializeWrite implements SerializeWrite { public static final Logger LOG = LoggerFactory.getLogger(LazyBinarySerializeWrite.class.getName()); private Output output; diff --git storage-api/src/java/org/apache/hadoop/hive/ql/exec/vector/BytesColumnVector.java storage-api/src/java/org/apache/hadoop/hive/ql/exec/vector/BytesColumnVector.java index 99744cd..fc4ec33 100644 --- storage-api/src/java/org/apache/hadoop/hive/ql/exec/vector/BytesColumnVector.java +++ storage-api/src/java/org/apache/hadoop/hive/ql/exec/vector/BytesColumnVector.java @@ -170,6 +170,23 @@ public void setVal(int elementNum, byte[] sourceBuf) { } /** + * A variation on setVal that allocates room for the value but lets the caller do the copy. + * Afterward, the caller must immediately set the bytes referenced by vector[elementNum] for + * the correct length at the start index. + * @param elementNum index within column vector to set + * @param length length of source byte sequence + */ + public void allocateVal(int elementNum, int length) { + if ((nextFree + length) > buffer.length) { + increaseBufferSpace(length); + } + vector[elementNum] = buffer; + this.start[elementNum] = nextFree; + this.length[elementNum] = length; + nextFree += length; + } + + /** * Set a field to the concatenation of two string values. Result data is copied * into the internal buffer. *